
!cp /content/drive/MyDrive/Colab/Data/'Готовые задачи'/Калининград/participants/train/train.csv ./ 
!cp /content/drive/MyDrive/Colab/Data/'Готовые задачи'/Калининград/participants/test/test.csv ./

cp: cannot stat '/content/drive/MyDrive/Colab/Data/'\'''$'\320\223\320\276\321\202\320\276\320\262\321\213\320\265'' '$'\320\267\320\260\320\264\320\260\321\207\320\270'\''/'$'\320\232\320\260\320\273\320\270\320\275\320\270\320\275\320\263\321\200\320\260\320\264''/participants/train/train.csv': No such file or directory
cp: cannot stat '/content/drive/MyDrive/Colab/Data/'\'''$'\320\223\320\276\321\202\320\276\320\262\321\213\320\265'' '$'\320\267\320\260\320\264\320\260\321\207\320\270'\''/'$'\320\232\320\260\320\273\320\270\320\275\320\270\320\275\320\263\321\200\320\260\320\264''/participants/test/test.csv': No such file or directory


#Установка catboost
!pip install catboost

Collecting catboost

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  Downloading catboost-1.0.6-cp38-none-win_amd64.whl (73.9 MB)
Collecting plotly
  Downloading plotly-5.9.0-py2.py3-none-any.whl (15.2 MB)
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  Downloading graphviz-0.20.1-py3-none-any.whl (47 kB)
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Collecting tenacity>=6.2.0
  Using cached tenacity-8.0.1-py3-none-any.whl (24 kB)
Installing collected packages: tenacity, plotly, graphviz, catboost
Successfully installed catboost-1.0.6 graphviz-0.20.1 plotly-5.9.0 tenacity-8.0.1


#import необходимых модулей

import pandas as pd
from catboost import CatBoostRegressor, Pool
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score


#Считывание данных в DataFrame 

train = pd.read_csv('train.csv', sep=';', index_col=None, dtype={'PATIENT_SEX':str, 'MKB_CODE':str, 'ADRES':str, 'VISIT_MONTH_YEAR':str, 'AGE_CATEGORY':str, 'PATIENT_ID_COUNT':int})
test = pd.read_csv('test.csv', sep=';', index_col=None, dtype={'PATIENT_SEX':str, 'MKB_CODE':str, 'ADRES':str, 'VISIT_MONTH_YEAR':str, 'AGE_CATEGORY':str})


#Отделение меток от данных

X = train[['PATIENT_SEX', 'MKB_CODE', 'ADRES', 'VISIT_MONTH_YEAR', 'AGE_CATEGORY']]
y = train[['PATIENT_ID_COUNT']]


train['month_year'] = pd.to_datetime(train.loc[:, 'VISIT_MONTH_YEAR'], format='%m.%y', errors='ignore')


train


unique_months = X['VISIT_MONTH_YEAR'].unique().astype('str')


unique_months

array(['08.21', '03.20', '03.19', '01.22', '02.18', '03.22', '07.18',
       '09.21', '10.19', '12.21', '05.18', '01.19', '05.19', '12.20',
       '06.18', '10.20', '06.20', '11.19', '12.19', '02.19', '02.22',
       '04.19', '05.21', '07.21', '01.20', '01.18', '02.20', '03.18',
       '03.21', '04.18', '04.20', '04.21', '05.20', '06.19', '06.21',
       '07.19', '07.20', '08.18', '08.19', '08.20', '09.18', '09.19',
       '09.20', '10.18', '10.21', '11.18', '11.20', '11.21', '12.18',
       '01.21', '02.21'], dtype='<U5')


df_uniq_months = pd.to_datetime(unique_months, format='%m.%y', errors='ignore')


df_uniq_months.sort_values()

DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',
               '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',
               '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01',
               '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01',
               '2022-01-01', '2022-02-01', '2022-03-01'],
              dtype='datetime64[ns]', freq=None)


df_uniq_months.sort_values(ascending=True)

DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',
               '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',
               '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01',
               '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01',
               '2022-01-01', '2022-02-01', '2022-03-01'],
              dtype='datetime64[ns]', freq=None)


uniq_city = X['ADRES'].unique()


print(uniq_city)

['Гурьевск' 'Калининград' 'Гусев' 'Нестеров' 'Партизанское' 'Садовое'
 'Славск' 'Ясная Поляна' 'Гвардейск' 'Холмогоровка' 'Багратионовск'
 'Большое Исаково' 'Зеленоградск' 'Калининградская' 'Колосовка'
 'Краснознаменск' 'Невское' 'Неман' 'Пионерский' 'Правдинск' 'Прибрежный'
 'СТ Железнодорожник' 'СТ Искра ул. Тюльпановая' 'СТ Победа' 'Светлый'
 'Советск' 'Совхозное' 'Черняховск' 'Балтийск' 'Малое Васильково'
 'Полесск' 'Сосновка' 'Ладушкин' 'Малинники' 'Раздольное' 'Озерск'
 'Волочаевское' 'Мамоново' 'Нивенское' 'Светлогорск' 'Васильково'
 'Голубево' 'Долгоруково' 'Донское' 'Дружный' 'Заозерье' 'Корнево'
 'Ласкино' 'Луговое' 'Люблино' 'Малиновка' 'Малое Исаково' 'Матросово'
 'Озерки' 'Переславское' 'Пятидорожное' 'СТ Колосок' 'СТ Радуга'
 'Саранское' 'Славинск' 'Славянское' 'Ушаково' 'Янтарный' 'Ясное'
 'Илюшино' 'Шоссейное' 'Южный' 'Петрово' 'Некрасово' 'Березовка'
 'Большаково' 'Высокое' 'Домново' 'Дорожный' 'Железнодорожный' 'Знаменск'
 'Коврово' 'Комсомольск' 'Константиновка' 'Крылово' 'Кумачево' 'Майское'
 'Мельниково' 'Муромское' 'Низовье' 'Новодорожный' 'Новостроево'
 'Первомайское' 'Пригородное' 'Приморск' 'Родники' 'Романово' 'Рощино'
 'Рыбачий' 'СТ Чайка' 'Тимирязево' 'Храброво' 'Лесное' 'Взморье'
 'Приморье' 'Рассвет' 'Залесье' 'Гурьевский' 'Фурманово' 'Заливино'
 'Малое Луговое' 'Покровское' 'Прибрежное' 'Рыбное' 'Орловка'
 'Владимирово' 'Добрино' 'Заостровье' 'Кострово' 'Маршальское'
 'Ново-Московское' 'Северный' 'Тургенево']


# TODO: get population by city name


# TODO: get lat, lon by city name


# TODO: get weather record by city name


# TODO: research periodicity


# TODO: EDA


train.head()


train.groupby('PATIENT_SEX').sum('PATIENT_ID_COUNT')


train.groupby('AGE_CATEGORY').sum('PATIENT_ID_COUNT').sort_values(by='PATIENT_ID_COUNT', ascending=False)


train.groupby('ADRES').sum('PATIENT_ID_COUNT').sort_values(by='PATIENT_ID_COUNT', ascending=False)


train.groupby('VISIT_MONTH_YEAR').sum('PATIENT_ID_COUNT').sort_values(by='PATIENT_ID_COUNT', ascending=False)


import matplotlib.pyplot as plt


mkb_count = train.groupby('MKB_CODE').sum('PATIENT_ID_COUNT').reset_index()


mkb_count.describe()


mkb_count.index

RangeIndex(start=0, stop=7644, step=1)


mkb_count.sort_values(by=['PATIENT_ID_COUNT'], ascending=False, inplace=True)


mkb_count.head()


mkb_count.query('PATIENT_ID_COUNT > 10000')


mkb_count.query('PATIENT_ID_COUNT > 10000').iloc[0:10,0]

2847    J06.9
7268    Z25.8
7123    Z00.0
2506    I11.9
3066    K02.1
7200    Z11.5
7150    Z02.7
7124    Z00.1
2833    J02.9
7134    Z01.2
Name: MKB_CODE, dtype: object


type(mkb_count.query('PATIENT_ID_COUNT > 10000').iloc[:, 0])

pandas.core.series.Series


mkb_top_list = mkb_count.query('PATIENT_ID_COUNT > 10000').iloc[:, 0].tolist()


mkb_top_list

['J06.9',
 'Z25.8',
 'Z00.0',
 'I11.9',
 'K02.1',
 'Z11.5',
 'Z02.7',
 'Z00.1',
 'J02.9',
 'Z01.2',
 'K04.5',
 'J00',
 'M42.1',
 'K04.0',
 'Z01.4',
 'J06.8',
 'Z01.7',
 'I67.8',
 'Z00',
 'J04.1',
 'Z11.3',
 'N60.1',
 'Z01.0',
 'K04.4',
 'Z20.8',
 'H52.1',
 'K02.8',
 'Z11.1',
 'J20.9',
 'Z01.1',
 'Z02.5',
 'N95.2',
 'Z00.8',
 'I67.9',
 'I67.2',
 'M54.4',
 'Z71.2',
 'Z25.1',
 'O99.8',
 'G93.4',
 'H40.1',
 'G90.8',
 'Z01.8',
 'K59.9',
 'K07.3',
 'I25.8',
 'N40',
 'H25.0',
 'F10.2',
 'H61.2',
 'G90.9',
 'Z02.0',
 'I25.1',
 'M54.5',
 'Z76.0',
 'K02.0',
 'Z27.3',
 'J18.9',
 'I83.9',
 'N76.0',
 'K07.2',
 'Z27.8',
 'M95.8',
 'M54.2',
 'I20.8',
 'I25.2',
 'M42.9',
 'M17.0',
 'K29.9',
 'C61',
 'H90.3',
 'Z34.8',
 'E06.3',
 'L23.9',
 'J45.8',
 'Z03.8',
 'B01.9',
 'H35.3',
 'F20.0',
 'E04.2',
 'M54.1',
 'C50.4',
 'M17.1',
 'H25.8',
 'J35.0',
 'K81.1',
 'M21.4',
 'I70.2',
 'H52.0',
 'H52.2',
 'I10',
 'B07',
 'J03.9',
 'O99.0',
 'S93.4',
 'D25.1',
 'Z00.4',
 'Z03.0',
 'K29.5',
 'M51.1',
 'S60.0',
 'N95.1',
 'J04.2',
 'I11.0',
 'Z27.4',
 'N72',
 'M15.0',
 'Z04.8',
 'M21.0',
 'Z20.1',
 'K86.1',
 'O26.0',
 'J02',
 'N20.0',
 'Z24.6',
 'Z34.0',
 'S20.2',
 'I69.3',
 'D50.9',
 'Z32.1',
 'S52.5',
 'N60.9',
 'K00.7',
 'Z51.8',
 'K58.9',
 'E03.8',
 'J12.8',
 'L70.0',
 'K29.7',
 'K05.3',
 'G25.9',
 'H65.0',
 'S00.8',
 'K82.8',
 'H10.0',
 'N76.1',
 'E66.0',
 'N81.2',
 'D24',
 'C50.9',
 'J30.4',
 'J30.0',
 'T15.0',
 'M54.6',
 'S80.0',
 'K21.0',
 'N86',
 'S90.0',
 'J01.0',
 'J31.2',
 'E04.1',
 'L24.9',
 'B35.1',
 'Z24.0',
 'N92.6',
 'K29.6',
 'N41.1',
 'H35.0',
 'K59.0',
 'B18.2',
 'M15.9',
 'Z27.1',
 'J35.2',
 'J34.2',
 'N84.0',
 'M19.0',
 'R76.1',
 'O23.5',
 'M13.9',
 'M77.3',
 'I11',
 'N60.8',
 'I25.9',
 'M41.1',
 'M16.1',
 'H52.4',
 'D23.5',
 'S61.0',
 'N81.1',
 'M16.0',
 'K80.1',
 'I67.4',
 'H52.5',
 'G35',
 'J44.8',
 'L40.0',
 'N30.0',
 'O20.0',
 'K10.2',
 'J06.0',
 'J45.0',
 'J42',
 'Z03.3',
 'N70.1',
 'J04.0',
 'J41.0',
 'G20',
 'H65.1',
 'K42.9',
 'G93.8',
 'F11.2',
 'M54.9']


!pip install icd10-cm

Collecting icd10-cm

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  Downloading icd10_cm-0.0.4-py2.py3-none-any.whl (675 kB)
Installing collected packages: icd10-cm
Successfully installed icd10-cm-0.0.4


import icd10

code = icd10.find("J20.0")
print(code.description)         # Acute bronchitis due to Mycoplasma pneumoniae
if code.billable:
    print(code, "is billable")  # J20.0 is billable

print(code.chapter)             # X
print(code.block)               # J00-J99
print(code.block_description)   # Diseases of the respiratory system

Acute bronchitis due to Mycoplasma pneumoniae
J20.0 is billable
X
J00-J99
Diseases of the respiratory system


import icd10

if icd10.exists("J20.0"):
    print("Exists")

Exists


mkb_top_name_dict = {}


for mkb in mkb_top_list:
    code = icd10.find(mkb)
    mkb_top_name_dict[mkb] = code.description if code is not None else 'NOT FOUND'


mkb_top_name_dict

{'J06.9': 'Acute upper respiratory infection, unspecified',
 'Z25.8': 'NOT FOUND',
 'Z00.0': 'Encounter for general adult medical examination',
 'I11.9': 'Hypertensive heart disease without heart failure',
 'K02.1': 'NOT FOUND',
 'Z11.5': 'Encounter for screening for other viral diseases',
 'Z02.7': 'Encounter for issue of medical certificate',
 'Z00.1': 'Encounter for newborn, infant and child health examinations',
 'J02.9': 'Acute pharyngitis, unspecified',
 'Z01.2': 'Encounter for dental examination and cleaning',
 'K04.5': 'Chronic apical periodontitis',
 'J00': 'Acute nasopharyngitis [common cold]',
 'M42.1': 'Adult osteochondrosis of spine',
 'K04.0': 'Pulpitis',
 'Z01.4': 'Encounter for gynecological examination',
 'J06.8': 'NOT FOUND',
 'Z01.7': 'NOT FOUND',
 'I67.8': 'Other specified cerebrovascular diseases',
 'Z00': 'Encounter for general examination without complaint, suspected or reported diagnosis',
 'J04.1': 'Acute tracheitis',
 'Z11.3': 'Encounter for screening for infections with a predominantly sexual mode of transmission',
 'N60.1': 'Diffuse cystic mastopathy',
 'Z01.0': 'Encounter for examination of eyes and vision',
 'K04.4': 'Acute apical periodontitis of pulpal origin',
 'Z20.8': 'Contact with and (suspected) exposure to other communicable diseases',
 'H52.1': 'Myopia',
 'K02.8': 'NOT FOUND',
 'Z11.1': 'Encounter for screening for respiratory tuberculosis',
 'J20.9': 'Acute bronchitis, unspecified',
 'Z01.1': 'Encounter for examination of ears and hearing',
 'Z02.5': 'Encounter for examination for participation in sport',
 'N95.2': 'Postmenopausal atrophic vaginitis',
 'Z00.8': 'Encounter for other general examination',
 'I67.9': 'Cerebrovascular disease, unspecified',
 'I67.2': 'Cerebral atherosclerosis',
 'M54.4': 'Lumbago with sciatica',
 'Z71.2': 'Person consulting for explanation of examination or test findings',
 'Z25.1': 'NOT FOUND',
 'O99.8': 'Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium',
 'G93.4': 'Other and unspecified encephalopathy',
 'H40.1': 'Open-angle glaucoma',
 'G90.8': 'Other disorders of autonomic nervous system',
 'Z01.8': 'Encounter for other specified special examinations',
 'K59.9': 'Functional intestinal disorder, unspecified',
 'K07.3': 'NOT FOUND',
 'I25.8': 'Other forms of chronic ischemic heart disease',
 'N40': 'Benign prostatic hyperplasia',
 'H25.0': 'Age-related incipient cataract',
 'F10.2': 'Alcohol dependence',
 'H61.2': 'Impacted cerumen',
 'G90.9': 'Disorder of the autonomic nervous system, unspecified',
 'Z02.0': 'Encounter for examination for admission to educational institution',
 'I25.1': 'Atherosclerotic heart disease of native coronary artery',
 'M54.5': 'Low back pain',
 'Z76.0': 'Encounter for issue of repeat prescription',
 'K02.0': 'NOT FOUND',
 'Z27.3': 'NOT FOUND',
 'J18.9': 'Pneumonia, unspecified organism',
 'I83.9': 'Asymptomatic varicose veins of lower extremities',
 'N76.0': 'Acute vaginitis',
 'K07.2': 'NOT FOUND',
 'Z27.8': 'NOT FOUND',
 'M95.8': 'Other specified acquired deformities of musculoskeletal system',
 'M54.2': 'Cervicalgia',
 'I20.8': 'Other forms of angina pectoris',
 'I25.2': 'Old myocardial infarction',
 'M42.9': 'Spinal osteochondrosis, unspecified',
 'M17.0': 'Bilateral primary osteoarthritis of knee',
 'K29.9': 'Gastroduodenitis, unspecified',
 'C61': 'Malignant neoplasm of prostate',
 'H90.3': 'Sensorineural hearing loss, bilateral',
 'Z34.8': 'Encounter for supervision of other normal pregnancy',
 'E06.3': 'Autoimmune thyroiditis',
 'L23.9': 'Allergic contact dermatitis, unspecified cause',
 'J45.8': 'NOT FOUND',
 'Z03.8': 'Encounter for observation for other suspected diseases and conditions ruled out',
 'B01.9': 'Varicella without complication',
 'H35.3': 'Degeneration of macula and posterior pole',
 'F20.0': 'Paranoid schizophrenia',
 'E04.2': 'Nontoxic multinodular goiter',
 'M54.1': 'Radiculopathy',
 'C50.4': 'Malignant neoplasm of upper-outer quadrant of breast',
 'M17.1': 'Unilateral primary osteoarthritis of knee',
 'H25.8': 'Other age-related cataract',
 'J35.0': 'Chronic tonsillitis and adenoiditis',
 'K81.1': 'Chronic cholecystitis',
 'M21.4': 'Flat foot [pes planus] (acquired)',
 'I70.2': 'Atherosclerosis of native arteries of the extremities',
 'H52.0': 'Hypermetropia',
 'H52.2': 'Astigmatism',
 'I10': 'Essential (primary) hypertension',
 'B07': 'Viral warts',
 'J03.9': 'Acute tonsillitis, unspecified',
 'O99.0': 'Anemia complicating pregnancy, childbirth and the puerperium',
 'S93.4': 'Sprain of ankle',
 'D25.1': 'Intramural leiomyoma of uterus',
 'Z00.4': 'NOT FOUND',
 'Z03.0': 'NOT FOUND',
 'K29.5': 'Unspecified chronic gastritis',
 'M51.1': 'Thoracic, thoracolumbar and lumbosacral intervertebral disc disorders with radiculopathy',
 'S60.0': 'Contusion of finger without damage to nail',
 'N95.1': 'Menopausal and female climacteric states',
 'J04.2': 'Acute laryngotracheitis',
 'I11.0': 'Hypertensive heart disease with heart failure',
 'Z27.4': 'NOT FOUND',
 'N72': 'Inflammatory disease of cervix uteri',
 'M15.0': 'Primary generalized (osteo)arthritis',
 'Z04.8': 'Encounter for examination and observation for other specified reasons',
 'M21.0': 'Valgus deformity, not elsewhere classified',
 'Z20.1': 'Contact with and (suspected) exposure to tuberculosis',
 'K86.1': 'Other chronic pancreatitis',
 'O26.0': 'Excessive weight gain in pregnancy',
 'J02': 'Acute pharyngitis',
 'N20.0': 'Calculus of kidney',
 'Z24.6': 'NOT FOUND',
 'Z34.0': 'Encounter for supervision of normal first pregnancy',
 'S20.2': 'Contusion of thorax',
 'I69.3': 'Sequelae of cerebral infarction',
 'D50.9': 'Iron deficiency anemia, unspecified',
 'Z32.1': 'NOT FOUND',
 'S52.5': 'Fracture of lower end of radius',
 'N60.9': 'Unspecified benign mammary dysplasia',
 'K00.7': 'Teething syndrome',
 'Z51.8': 'Encounter for other specified aftercare',
 'K58.9': 'Irritable bowel syndrome without diarrhea',
 'E03.8': 'Other specified hypothyroidism',
 'J12.8': 'Other viral pneumonia',
 'L70.0': 'Acne vulgaris',
 'K29.7': 'Gastritis, unspecified',
 'K05.3': 'Chronic periodontitis',
 'G25.9': 'Extrapyramidal and movement disorder, unspecified',
 'H65.0': 'Acute serous otitis media',
 'S00.8': 'Superficial injury of other parts of head',
 'K82.8': 'Other specified diseases of gallbladder',
 'H10.0': 'Mucopurulent conjunctivitis',
 'N76.1': 'Subacute and chronic vaginitis',
 'E66.0': 'Obesity due to excess calories',
 'N81.2': 'Incomplete uterovaginal prolapse',
 'D24': 'Benign neoplasm of breast',
 'C50.9': 'Malignant neoplasm of breast of unspecified site',
 'J30.4': 'NOT FOUND',
 'J30.0': 'Vasomotor rhinitis',
 'T15.0': 'Foreign body in cornea',
 'M54.6': 'Pain in thoracic spine',
 'S80.0': 'Contusion of knee',
 'K21.0': 'Gastro-esophageal reflux disease with esophagitis',
 'N86': 'Erosion and ectropion of cervix uteri',
 'S90.0': 'Contusion of ankle',
 'J01.0': 'Acute maxillary sinusitis',
 'J31.2': 'Chronic pharyngitis',
 'E04.1': 'Nontoxic single thyroid nodule',
 'L24.9': 'Irritant contact dermatitis, unspecified cause',
 'B35.1': 'Tinea unguium',
 'Z24.0': 'NOT FOUND',
 'N92.6': 'Irregular menstruation, unspecified',
 'K29.6': 'Other gastritis',
 'N41.1': 'Chronic prostatitis',
 'H35.0': 'Background retinopathy and retinal vascular changes',
 'K59.0': 'Constipation',
 'B18.2': 'Chronic viral hepatitis C',
 'M15.9': 'Polyosteoarthritis, unspecified',
 'Z27.1': 'NOT FOUND',
 'J35.2': 'Hypertrophy of adenoids',
 'J34.2': 'Deviated nasal septum',
 'N84.0': 'Polyp of corpus uteri',
 'M19.0': 'Primary osteoarthritis of other joints',
 'R76.1': 'Nonspecific reaction to test for tuberculosis',
 'O23.5': 'Infections of the genital tract in pregnancy',
 'M13.9': 'NOT FOUND',
 'M77.3': 'Calcaneal spur',
 'I11': 'Hypertensive heart disease',
 'N60.8': 'Other benign mammary dysplasias',
 'I25.9': 'Chronic ischemic heart disease, unspecified',
 'M41.1': 'Juvenile and adolescent idiopathic scoliosis',
 'M16.1': 'Unilateral primary osteoarthritis of hip',
 'H52.4': 'Presbyopia',
 'D23.5': 'Other benign neoplasm of skin of trunk',
 'S61.0': 'Open wound of thumb without damage to nail',
 'N81.1': 'Cystocele',
 'M16.0': 'Bilateral primary osteoarthritis of hip',
 'K80.1': 'Calculus of gallbladder with other cholecystitis',
 'I67.4': 'Hypertensive encephalopathy',
 'H52.5': 'Disorders of accommodation',
 'G35': 'Multiple sclerosis',
 'J44.8': 'NOT FOUND',
 'L40.0': 'Psoriasis vulgaris',
 'N30.0': 'Acute cystitis',
 'O20.0': 'Threatened abortion',
 'K10.2': 'NOT FOUND',
 'J06.0': 'Acute laryngopharyngitis',
 'J45.0': 'NOT FOUND',
 'J42': 'Unspecified chronic bronchitis',
 'Z03.3': 'NOT FOUND',
 'N70.1': 'Chronic salpingitis and oophoritis',
 'J04.0': 'Acute laryngitis',
 'J41.0': 'Simple chronic bronchitis',
 'G20': "Parkinson's disease",
 'H65.1': 'Other acute nonsuppurative otitis media',
 'K42.9': 'Umbilical hernia without obstruction or gangrene',
 'G93.8': 'Other specified disorders of brain',
 'F11.2': 'Opioid dependence',
 'M54.9': 'Dorsalgia, unspecified'}


mkb_top_name_dict['K58.9']

'Irritable bowel syndrome without diarrhea'


!pip install simple-icd-10

Collecting simple-icd-10

WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Error parsing requirements for jupyter-packaging: [Errno 2] No such file or directory: 'd:\\programdata\\anaconda3\\lib\\site-packages\\jupyter_packaging-0.7.12.dist-info\\METADATA'
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)

  Downloading simple_icd_10-2.0.1-py3-none-any.whl (173 kB)
Installing collected packages: simple-icd-10
Successfully installed simple-icd-10-2.0.1


import simple_icd_10 as icd


icd.get_description("XII")

'Diseases of the skin and subcutaneous tissue'


mkb_top_name_simple_dict = {}
cnt_err = 0
for mkb in mkb_top_list:
    try:
        mkb_desc = icd.get_description(mkb)
        
        mkb_top_name_simple_dict[mkb] = mkb_desc
    except ValueError as e:
        mkb_top_name_simple_dict[mkb] = 'NOT FOUND'
        print(mkb)
        cnt_err += 1

print(cnt_err)

K58.9
1


mkb_top_name_simple_dict

{'J06.9': 'Acute upper respiratory infection, unspecified',
 'Z25.8': 'Need for immunization against other specified single viral diseases',
 'Z00.0': 'General medical examination',
 'I11.9': 'Hypertensive heart disease without (congestive) heart failure',
 'K02.1': 'Caries of dentine',
 'Z11.5': 'Special screening examination for other viral diseases',
 'Z02.7': 'Issue of medical certificate',
 'Z00.1': 'Routine child health examination',
 'J02.9': 'Acute pharyngitis, unspecified',
 'Z01.2': 'Dental examination',
 'K04.5': 'Chronic apical periodontitis',
 'J00': 'Acute nasopharyngitis [common cold]',
 'M42.1': 'Adult osteochondrosis of spine',
 'K04.0': 'Pulpitis',
 'Z01.4': 'Gynaecological examination (general)(routine)',
 'J06.8': 'Other acute upper respiratory infections of multiple sites',
 'Z01.7': 'Laboratory examination',
 'I67.8': 'Other specified cerebrovascular diseases',
 'Z00': 'General examination and investigation of persons without complaint and reported diagnosis',
 'J04.1': 'Acute tracheitis',
 'Z11.3': 'Special screening examination for infections with a predominantly sexual mode of transmission',
 'N60.1': 'Diffuse cystic mastopathy',
 'Z01.0': 'Examination of eyes and vision',
 'K04.4': 'Acute apical periodontitis of pulpal origin',
 'Z20.8': 'Contact with and exposure to other communicable diseases',
 'H52.1': 'Myopia',
 'K02.8': 'Other dental caries',
 'Z11.1': 'Special screening examination for respiratory tuberculosis',
 'J20.9': 'Acute bronchitis, unspecified',
 'Z01.1': 'Examination of ears and hearing',
 'Z02.5': 'Examination for participation in sport',
 'N95.2': 'Postmenopausal atrophic vaginitis',
 'Z00.8': 'Other general examinations',
 'I67.9': 'Cerebrovascular disease, unspecified',
 'I67.2': 'Cerebral atherosclerosis',
 'M54.4': 'Lumbago with sciatica',
 'Z71.2': 'Person consulting for explanation of investigation findings',
 'Z25.1': 'Need for immunization against influenza',
 'O99.8': 'Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium',
 'G93.4': 'Encephalopathy, unspecified',
 'H40.1': 'Primary open-angle glaucoma',
 'G90.8': 'Other disorders of autonomic nervous system',
 'Z01.8': 'Other specified special examinations',
 'K59.9': 'Functional intestinal disorder, unspecified',
 'K07.3': 'Anomalies of tooth position',
 'I25.8': 'Other forms of chronic ischaemic heart disease',
 'N40': 'Hyperplasia of prostate',
 'H25.0': 'Senile incipient cataract',
 'F10.2': 'Mental and behavioural disorders due to use of alcohol : dependence syndrome',
 'H61.2': 'Impacted cerumen',
 'G90.9': 'Disorder of autonomic nervous system, unspecified',
 'Z02.0': 'Examination for admission to educational institution',
 'I25.1': 'Atherosclerotic heart disease',
 'M54.5': 'Low back pain',
 'Z76.0': 'Issue of repeat prescription',
 'K02.0': 'Caries limited to enamel',
 'Z27.3': 'Need for immunization against diphtheria-tetanus-pertussis with poliomyelitis [DTP + polio]',
 'J18.9': 'Pneumonia, unspecified',
 'I83.9': 'Varicose veins of lower extremities without ulcer or inflammation',
 'N76.0': 'Acute vaginitis',
 'K07.2': 'Anomalies of dental arch relationship',
 'Z27.8': 'Need for immunization against other combinations of infectious diseases',
 'M95.8': 'Other specified acquired deformities of musculoskeletal system',
 'M54.2': 'Cervicalgia',
 'I20.8': 'Other forms of angina pectoris',
 'I25.2': 'Old myocardial infarction',
 'M42.9': 'Spinal osteochondrosis, unspecified',
 'M17.0': 'Primary gonarthrosis, bilateral',
 'K29.9': 'Gastroduodenitis, unspecified',
 'C61': 'Malignant neoplasm of prostate',
 'H90.3': 'Sensorineural hearing loss, bilateral',
 'Z34.8': 'Supervision of other normal pregnancy',
 'E06.3': 'Autoimmune thyroiditis',
 'L23.9': 'Allergic contact dermatitis, unspecified cause',
 'J45.8': 'Mixed asthma',
 'Z03.8': 'Observation for other suspected diseases and conditions',
 'B01.9': 'Varicella without complication',
 'H35.3': 'Degeneration of macula and posterior pole',
 'F20.0': 'Paranoid schizophrenia',
 'E04.2': 'Nontoxic multinodular goitre',
 'M54.1': 'Radiculopathy',
 'C50.4': 'Malignant neoplasm: Upper-outer quadrant of breast',
 'M17.1': 'Other primary gonarthrosis',
 'H25.8': 'Other senile cataract',
 'J35.0': 'Chronic tonsillitis',
 'K81.1': 'Chronic cholecystitis',
 'M21.4': 'Flat foot [pes planus] (acquired)',
 'I70.2': 'Atherosclerosis of arteries of extremities',
 'H52.0': 'Hypermetropia',
 'H52.2': 'Astigmatism',
 'I10': 'Essential (primary) hypertension',
 'B07': 'Viral warts',
 'J03.9': 'Acute tonsillitis, unspecified',
 'O99.0': 'Anaemia complicating pregnancy, childbirth and the puerperium',
 'S93.4': 'Sprain and strain of ankle',
 'D25.1': 'Intramural leiomyoma of uterus',
 'Z00.4': 'General psychiatric examination, not elsewhere classified',
 'Z03.0': 'Observation for suspected tuberculosis',
 'K29.5': 'Chronic gastritis, unspecified',
 'M51.1': 'Lumbar and other intervertebral disc disorders with radiculopathy',
 'S60.0': 'Contusion of finger(s) without damage to nail',
 'N95.1': 'Menopausal and female climacteric states',
 'J04.2': 'Acute laryngotracheitis',
 'I11.0': 'Hypertensive heart disease with (congestive) heart failure',
 'Z27.4': 'Need for immunization against measles-mumps-rubella [MMR]',
 'N72': 'Inflammatory disease of cervix uteri',
 'M15.0': 'Primary generalized (osteo)arthrosis',
 'Z04.8': 'Examination and observation for other specified reasons',
 'M21.0': 'Valgus deformity, not elsewhere classified',
 'Z20.1': 'Contact with and exposure to tuberculosis',
 'K86.1': 'Other chronic pancreatitis',
 'O26.0': 'Excessive weight gain in pregnancy',
 'J02': 'Acute pharyngitis',
 'N20.0': 'Calculus of kidney',
 'Z24.6': 'Need for immunization against viral hepatitis',
 'Z34.0': 'Supervision of normal first pregnancy',
 'S20.2': 'Contusion of thorax',
 'I69.3': 'Sequelae of cerebral infarction',
 'D50.9': 'Iron deficiency anaemia, unspecified',
 'Z32.1': 'Pregnancy confirmed',
 'S52.5': 'Fracture of lower end of radius',
 'N60.9': 'Benign mammary dysplasia, unspecified',
 'K00.7': 'Teething syndrome',
 'Z51.8': 'Other specified medical care',
 'K58.9': 'NOT FOUND',
 'E03.8': 'Other specified hypothyroidism',
 'J12.8': 'Other viral pneumonia',
 'L70.0': 'Acne vulgaris',
 'K29.7': 'Gastritis, unspecified',
 'K05.3': 'Chronic periodontitis',
 'G25.9': 'Extrapyramidal and movement disorder, unspecified',
 'H65.0': 'Acute serous otitis media',
 'S00.8': 'Superficial injury of other parts of head',
 'K82.8': 'Other specified diseases of gallbladder',
 'H10.0': 'Mucopurulent conjunctivitis',
 'N76.1': 'Subacute and chronic vaginitis',
 'E66.0': 'Obesity due to excess calories',
 'N81.2': 'Incomplete uterovaginal prolapse',
 'D24': 'Benign neoplasm of breast',
 'C50.9': 'Malignant neoplasm: Breast, unspecified',
 'J30.4': 'Allergic rhinitis, unspecified',
 'J30.0': 'Vasomotor rhinitis',
 'T15.0': 'Foreign body in cornea',
 'M54.6': 'Pain in thoracic spine',
 'S80.0': 'Contusion of knee',
 'K21.0': 'Gastro-oesophageal reflux disease with oesophagitis',
 'N86': 'Erosion and ectropion of cervix uteri',
 'S90.0': 'Contusion of ankle',
 'J01.0': 'Acute maxillary sinusitis',
 'J31.2': 'Chronic pharyngitis',
 'E04.1': 'Nontoxic single thyroid nodule',
 'L24.9': 'Irritant contact dermatitis, unspecified cause',
 'B35.1': 'Tinea unguium',
 'Z24.0': 'Need for immunization against poliomyelitis',
 'N92.6': 'Irregular menstruation, unspecified',
 'K29.6': 'Other gastritis',
 'N41.1': 'Chronic prostatitis',
 'H35.0': 'Background retinopathy and retinal vascular changes',
 'K59.0': 'Constipation',
 'B18.2': 'Chronic viral hepatitis C',
 'M15.9': 'Polyarthrosis, unspecified',
 'Z27.1': 'Need for immunization against diphtheria-tetanus-pertussis, combined [DTP]',
 'J35.2': 'Hypertrophy of adenoids',
 'J34.2': 'Deviated nasal septum',
 'N84.0': 'Polyp of corpus uteri',
 'M19.0': 'Primary arthrosis of other joints',
 'R76.1': 'Abnormal reaction to tuberculin test',
 'O23.5': 'Infections of the genital tract in pregnancy',
 'M13.9': 'Arthritis, unspecified',
 'M77.3': 'Calcaneal spur',
 'I11': 'Hypertensive heart disease',
 'N60.8': 'Other benign mammary dysplasias',
 'I25.9': 'Chronic ischaemic heart disease, unspecified',
 'M41.1': 'Juvenile idiopathic scoliosis',
 'M16.1': 'Other primary coxarthrosis',
 'H52.4': 'Presbyopia',
 'D23.5': 'Benign neoplasm: Skin of trunk',
 'S61.0': 'Open wound of finger(s) without damage to nail',
 'N81.1': 'Cystocele',
 'M16.0': 'Primary coxarthrosis, bilateral',
 'K80.1': 'Calculus of gallbladder with other cholecystitis',
 'I67.4': 'Hypertensive encephalopathy',
 'H52.5': 'Disorders of accommodation',
 'G35': 'Multiple sclerosis',
 'J44.8': 'Other specified chronic obstructive pulmonary disease',
 'L40.0': 'Psoriasis vulgaris',
 'N30.0': 'Acute cystitis',
 'O20.0': 'Threatened abortion',
 'K10.2': 'Inflammatory conditions of jaws',
 'J06.0': 'Acute laryngopharyngitis',
 'J45.0': 'Predominantly allergic asthma',
 'J42': 'Unspecified chronic bronchitis',
 'Z03.3': 'Observation for suspected nervous system disorder',
 'N70.1': 'Chronic salpingitis and oophoritis',
 'J04.0': 'Acute laryngitis',
 'J41.0': 'Simple chronic bronchitis',
 'G20': 'Parkinson disease',
 'H65.1': 'Other acute nonsuppurative otitis media',
 'K42.9': 'Umbilical hernia without obstruction or gangrene',
 'G93.8': 'Other specified disorders of brain',
 'F11.2': 'Mental and behavioural disorders due to use of opioids : dependence syndrome',
 'M54.9': 'Dorsalgia, unspecified'}


for mkb_code, mkb_name in mkb_top_name_simple_dict.items():
    if mkb_name == 'NOT FOUND':
        print(mkb_code, mkb_name, sep=':')

K58.9:NOT FOUND


mkb_top_name_simple_dict['K58.9'] = mkb_top_name_dict['K58.9']


for mkb_code, mkb_name in mkb_top_name_simple_dict.items():
    if mkb_name == 'NOT FOUND':
        print(mkb_code, mkb_name, sep=':')


mkb_top_name_simple_dict

{'J06.9': 'Acute upper respiratory infection, unspecified',
 'Z25.8': 'Need for immunization against other specified single viral diseases',
 'Z00.0': 'General medical examination',
 'I11.9': 'Hypertensive heart disease without (congestive) heart failure',
 'K02.1': 'Caries of dentine',
 'Z11.5': 'Special screening examination for other viral diseases',
 'Z02.7': 'Issue of medical certificate',
 'Z00.1': 'Routine child health examination',
 'J02.9': 'Acute pharyngitis, unspecified',
 'Z01.2': 'Dental examination',
 'K04.5': 'Chronic apical periodontitis',
 'J00': 'Acute nasopharyngitis [common cold]',
 'M42.1': 'Adult osteochondrosis of spine',
 'K04.0': 'Pulpitis',
 'Z01.4': 'Gynaecological examination (general)(routine)',
 'J06.8': 'Other acute upper respiratory infections of multiple sites',
 'Z01.7': 'Laboratory examination',
 'I67.8': 'Other specified cerebrovascular diseases',
 'Z00': 'General examination and investigation of persons without complaint and reported diagnosis',
 'J04.1': 'Acute tracheitis',
 'Z11.3': 'Special screening examination for infections with a predominantly sexual mode of transmission',
 'N60.1': 'Diffuse cystic mastopathy',
 'Z01.0': 'Examination of eyes and vision',
 'K04.4': 'Acute apical periodontitis of pulpal origin',
 'Z20.8': 'Contact with and exposure to other communicable diseases',
 'H52.1': 'Myopia',
 'K02.8': 'Other dental caries',
 'Z11.1': 'Special screening examination for respiratory tuberculosis',
 'J20.9': 'Acute bronchitis, unspecified',
 'Z01.1': 'Examination of ears and hearing',
 'Z02.5': 'Examination for participation in sport',
 'N95.2': 'Postmenopausal atrophic vaginitis',
 'Z00.8': 'Other general examinations',
 'I67.9': 'Cerebrovascular disease, unspecified',
 'I67.2': 'Cerebral atherosclerosis',
 'M54.4': 'Lumbago with sciatica',
 'Z71.2': 'Person consulting for explanation of investigation findings',
 'Z25.1': 'Need for immunization against influenza',
 'O99.8': 'Other specified diseases and conditions complicating pregnancy, childbirth and the puerperium',
 'G93.4': 'Encephalopathy, unspecified',
 'H40.1': 'Primary open-angle glaucoma',
 'G90.8': 'Other disorders of autonomic nervous system',
 'Z01.8': 'Other specified special examinations',
 'K59.9': 'Functional intestinal disorder, unspecified',
 'K07.3': 'Anomalies of tooth position',
 'I25.8': 'Other forms of chronic ischaemic heart disease',
 'N40': 'Hyperplasia of prostate',
 'H25.0': 'Senile incipient cataract',
 'F10.2': 'Mental and behavioural disorders due to use of alcohol : dependence syndrome',
 'H61.2': 'Impacted cerumen',
 'G90.9': 'Disorder of autonomic nervous system, unspecified',
 'Z02.0': 'Examination for admission to educational institution',
 'I25.1': 'Atherosclerotic heart disease',
 'M54.5': 'Low back pain',
 'Z76.0': 'Issue of repeat prescription',
 'K02.0': 'Caries limited to enamel',
 'Z27.3': 'Need for immunization against diphtheria-tetanus-pertussis with poliomyelitis [DTP + polio]',
 'J18.9': 'Pneumonia, unspecified',
 'I83.9': 'Varicose veins of lower extremities without ulcer or inflammation',
 'N76.0': 'Acute vaginitis',
 'K07.2': 'Anomalies of dental arch relationship',
 'Z27.8': 'Need for immunization against other combinations of infectious diseases',
 'M95.8': 'Other specified acquired deformities of musculoskeletal system',
 'M54.2': 'Cervicalgia',
 'I20.8': 'Other forms of angina pectoris',
 'I25.2': 'Old myocardial infarction',
 'M42.9': 'Spinal osteochondrosis, unspecified',
 'M17.0': 'Primary gonarthrosis, bilateral',
 'K29.9': 'Gastroduodenitis, unspecified',
 'C61': 'Malignant neoplasm of prostate',
 'H90.3': 'Sensorineural hearing loss, bilateral',
 'Z34.8': 'Supervision of other normal pregnancy',
 'E06.3': 'Autoimmune thyroiditis',
 'L23.9': 'Allergic contact dermatitis, unspecified cause',
 'J45.8': 'Mixed asthma',
 'Z03.8': 'Observation for other suspected diseases and conditions',
 'B01.9': 'Varicella without complication',
 'H35.3': 'Degeneration of macula and posterior pole',
 'F20.0': 'Paranoid schizophrenia',
 'E04.2': 'Nontoxic multinodular goitre',
 'M54.1': 'Radiculopathy',
 'C50.4': 'Malignant neoplasm: Upper-outer quadrant of breast',
 'M17.1': 'Other primary gonarthrosis',
 'H25.8': 'Other senile cataract',
 'J35.0': 'Chronic tonsillitis',
 'K81.1': 'Chronic cholecystitis',
 'M21.4': 'Flat foot [pes planus] (acquired)',
 'I70.2': 'Atherosclerosis of arteries of extremities',
 'H52.0': 'Hypermetropia',
 'H52.2': 'Astigmatism',
 'I10': 'Essential (primary) hypertension',
 'B07': 'Viral warts',
 'J03.9': 'Acute tonsillitis, unspecified',
 'O99.0': 'Anaemia complicating pregnancy, childbirth and the puerperium',
 'S93.4': 'Sprain and strain of ankle',
 'D25.1': 'Intramural leiomyoma of uterus',
 'Z00.4': 'General psychiatric examination, not elsewhere classified',
 'Z03.0': 'Observation for suspected tuberculosis',
 'K29.5': 'Chronic gastritis, unspecified',
 'M51.1': 'Lumbar and other intervertebral disc disorders with radiculopathy',
 'S60.0': 'Contusion of finger(s) without damage to nail',
 'N95.1': 'Menopausal and female climacteric states',
 'J04.2': 'Acute laryngotracheitis',
 'I11.0': 'Hypertensive heart disease with (congestive) heart failure',
 'Z27.4': 'Need for immunization against measles-mumps-rubella [MMR]',
 'N72': 'Inflammatory disease of cervix uteri',
 'M15.0': 'Primary generalized (osteo)arthrosis',
 'Z04.8': 'Examination and observation for other specified reasons',
 'M21.0': 'Valgus deformity, not elsewhere classified',
 'Z20.1': 'Contact with and exposure to tuberculosis',
 'K86.1': 'Other chronic pancreatitis',
 'O26.0': 'Excessive weight gain in pregnancy',
 'J02': 'Acute pharyngitis',
 'N20.0': 'Calculus of kidney',
 'Z24.6': 'Need for immunization against viral hepatitis',
 'Z34.0': 'Supervision of normal first pregnancy',
 'S20.2': 'Contusion of thorax',
 'I69.3': 'Sequelae of cerebral infarction',
 'D50.9': 'Iron deficiency anaemia, unspecified',
 'Z32.1': 'Pregnancy confirmed',
 'S52.5': 'Fracture of lower end of radius',
 'N60.9': 'Benign mammary dysplasia, unspecified',
 'K00.7': 'Teething syndrome',
 'Z51.8': 'Other specified medical care',
 'K58.9': 'Irritable bowel syndrome without diarrhea',
 'E03.8': 'Other specified hypothyroidism',
 'J12.8': 'Other viral pneumonia',
 'L70.0': 'Acne vulgaris',
 'K29.7': 'Gastritis, unspecified',
 'K05.3': 'Chronic periodontitis',
 'G25.9': 'Extrapyramidal and movement disorder, unspecified',
 'H65.0': 'Acute serous otitis media',
 'S00.8': 'Superficial injury of other parts of head',
 'K82.8': 'Other specified diseases of gallbladder',
 'H10.0': 'Mucopurulent conjunctivitis',
 'N76.1': 'Subacute and chronic vaginitis',
 'E66.0': 'Obesity due to excess calories',
 'N81.2': 'Incomplete uterovaginal prolapse',
 'D24': 'Benign neoplasm of breast',
 'C50.9': 'Malignant neoplasm: Breast, unspecified',
 'J30.4': 'Allergic rhinitis, unspecified',
 'J30.0': 'Vasomotor rhinitis',
 'T15.0': 'Foreign body in cornea',
 'M54.6': 'Pain in thoracic spine',
 'S80.0': 'Contusion of knee',
 'K21.0': 'Gastro-oesophageal reflux disease with oesophagitis',
 'N86': 'Erosion and ectropion of cervix uteri',
 'S90.0': 'Contusion of ankle',
 'J01.0': 'Acute maxillary sinusitis',
 'J31.2': 'Chronic pharyngitis',
 'E04.1': 'Nontoxic single thyroid nodule',
 'L24.9': 'Irritant contact dermatitis, unspecified cause',
 'B35.1': 'Tinea unguium',
 'Z24.0': 'Need for immunization against poliomyelitis',
 'N92.6': 'Irregular menstruation, unspecified',
 'K29.6': 'Other gastritis',
 'N41.1': 'Chronic prostatitis',
 'H35.0': 'Background retinopathy and retinal vascular changes',
 'K59.0': 'Constipation',
 'B18.2': 'Chronic viral hepatitis C',
 'M15.9': 'Polyarthrosis, unspecified',
 'Z27.1': 'Need for immunization against diphtheria-tetanus-pertussis, combined [DTP]',
 'J35.2': 'Hypertrophy of adenoids',
 'J34.2': 'Deviated nasal septum',
 'N84.0': 'Polyp of corpus uteri',
 'M19.0': 'Primary arthrosis of other joints',
 'R76.1': 'Abnormal reaction to tuberculin test',
 'O23.5': 'Infections of the genital tract in pregnancy',
 'M13.9': 'Arthritis, unspecified',
 'M77.3': 'Calcaneal spur',
 'I11': 'Hypertensive heart disease',
 'N60.8': 'Other benign mammary dysplasias',
 'I25.9': 'Chronic ischaemic heart disease, unspecified',
 'M41.1': 'Juvenile idiopathic scoliosis',
 'M16.1': 'Other primary coxarthrosis',
 'H52.4': 'Presbyopia',
 'D23.5': 'Benign neoplasm: Skin of trunk',
 'S61.0': 'Open wound of finger(s) without damage to nail',
 'N81.1': 'Cystocele',
 'M16.0': 'Primary coxarthrosis, bilateral',
 'K80.1': 'Calculus of gallbladder with other cholecystitis',
 'I67.4': 'Hypertensive encephalopathy',
 'H52.5': 'Disorders of accommodation',
 'G35': 'Multiple sclerosis',
 'J44.8': 'Other specified chronic obstructive pulmonary disease',
 'L40.0': 'Psoriasis vulgaris',
 'N30.0': 'Acute cystitis',
 'O20.0': 'Threatened abortion',
 'K10.2': 'Inflammatory conditions of jaws',
 'J06.0': 'Acute laryngopharyngitis',
 'J45.0': 'Predominantly allergic asthma',
 'J42': 'Unspecified chronic bronchitis',
 'Z03.3': 'Observation for suspected nervous system disorder',
 'N70.1': 'Chronic salpingitis and oophoritis',
 'J04.0': 'Acute laryngitis',
 'J41.0': 'Simple chronic bronchitis',
 'G20': 'Parkinson disease',
 'H65.1': 'Other acute nonsuppurative otitis media',
 'K42.9': 'Umbilical hernia without obstruction or gangrene',
 'G93.8': 'Other specified disorders of brain',
 'F11.2': 'Mental and behavioural disorders due to use of opioids : dependence syndrome',
 'M54.9': 'Dorsalgia, unspecified'}


mkb_count


mkb_top_plot = mkb_count.query('PATIENT_ID_COUNT > 10000').loc[:, ['MKB_CODE', 'PATIENT_ID_COUNT']].reset_index(drop=True)


plt.figure(figsize=(16, 8), dpi=80)
plt.bar(mkb_top_plot['MKB_CODE'], mkb_top_plot['PATIENT_ID_COUNT'])
plt.show()


train.groupby(['ADRES', 'MKB_CODE']).value_counts()

ADRES          MKB_CODE  PATIENT_SEX  VISIT_MONTH_YEAR  AGE_CATEGORY  PATIENT_ID_COUNT
Багратионовск  A02.0     0            12.20             elderly       1                   1
               A02.9     0            10.19             children      1                   1
               A04.3     0            02.22             middleage     1                   1
               A04.9     1            06.21             young         1                   1
                                      03.18             young         1                   1
                                                                                         ..
Ясное          Z45       1            04.18             middleage     1                   1
               Z54.0     0            01.19             young         1                   1
               Z71.2     1            04.19             young         1                   1
               Z71.8     0            07.18             centenarians  1                   1
                         1            07.18             middleage     1                   1
Length: 2212393, dtype: int64


top_mkb = train.query('ADRES == "Калининград" & MKB_CODE == "J06.9"')
top_mkb


top_mkb_f = top_mkb.query('PATIENT_SEX == "0"')
top_mkb_f


top_mkb_m = top_mkb.query('PATIENT_SEX == "1"')
top_mkb_m


list(range(2018, 2023))

[2018, 2019, 2020, 2021, 2022]


import datetime as dt
plt.figure(figsize=(12, 6), dpi=80)
plt.plot(top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = '+')
plt.plot(top_mkb_f.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = 'v')
plt.plot(top_mkb_m.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = '^')
[plt.axvline([dt.datetime(year, 1, 1)]) for year in range(2018, 2023)]
plt.grid()
plt.show()


plt.figure(figsize=(12, 6), dpi=80)

top_mkb_by_age = top_mkb.groupby('AGE_CATEGORY').groups.keys()
for age_group in top_mkb_by_age:
    age_group_filtered = top_mkb.query('AGE_CATEGORY == @age_group').sort_values(by='month_year', ascending=True)
    plt.plot(age_group_filtered.loc[:, 'month_year'], age_group_filtered.loc[:, 'PATIENT_ID_COUNT'], marker = 'o')

[plt.axvline([dt.datetime(year, 1, 1)]) for year in range(2018, 2023)]
plt.legend(list(top_mkb_by_age))
plt.grid()
plt.show()


# Moving Average
top_mkb_total = top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT')
#print(top_mkb_total)
moving_average = top_mkb_total.rolling(
    window=12,       # 365-day window
    center=True,      # puts the average at the center of the window
    min_periods=6,  # choose about half the window size
).mean()              # compute the mean (could also do median, std, min, max, ...)

ax = top_mkb_total.plot(figsize=(12, 6), style=".", color="0.5", grid=True)

moving_average.plot(
    ax=ax, linewidth=3, title="Top MKB Total Moving Average", legend=False,
);
[plt.axvline([dt.datetime(year, 1, 1)]) for year in range(2018, 2023)]
plt.grid()
plt.show()


import datetime as dt

# Coranavirus turning trend point at 2020-05-01
corona_turn = dt.datetime(2020, 5, 1)
corona_turn_idx = top_mkb_total[:corona_turn].shape[0]


top_mkb_total.index[0:corona_turn_idx]

DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01'],
              dtype='datetime64[ns]', name='month_year', freq=None)


from statsmodels.tsa.deterministic import DeterministicProcess

dp_prior = DeterministicProcess(
    index=top_mkb_total.index[:corona_turn_idx],  # dates from the training data
    constant=True,       # dummy feature for the bias (y_intercept)
    order=1,             # the time dummy (trend)
    drop=True,           # drop terms if necessary to avoid collinearity
)

dp_after = DeterministicProcess(
    index=top_mkb_total.index[corona_turn_idx-1:],  # dates from the training data
    constant=True,       # dummy feature for the bias (y_intercept)
    order=1,             # the time dummy (trend)
    drop=True,           # drop terms if necessary to avoid collinearity
)

# `in_sample` creates features for the dates given in the `index` argument
X_prior = dp_prior.in_sample()
X_after = dp_after.in_sample()

print(X_prior.tail())
print(X_after.head())

            const  trend
month_year              
2020-01-01    1.0   25.0
2020-02-01    1.0   26.0
2020-03-01    1.0   27.0
2020-04-01    1.0   28.0
2020-05-01    1.0   29.0
            const  trend
month_year              
2020-05-01    1.0    1.0
2020-06-01    1.0    2.0
2020-07-01    1.0    3.0
2020-08-01    1.0    4.0
2020-09-01    1.0    5.0


dp_after.out_of_sample(1)


from sklearn.linear_model import LinearRegression

y_prior = top_mkb_total['PATIENT_ID_COUNT'][:corona_turn_idx]  # the target
y_after = top_mkb_total['PATIENT_ID_COUNT'][corona_turn_idx-1:]  # the target


# The intercept is the same as the `const` feature from
# DeterministicProcess. LinearRegression behaves badly with duplicated
# features, so we need to be sure to exclude it here.
model_prior = LinearRegression(fit_intercept=False)
model_prior.fit(X_prior, y_prior)

model_after = LinearRegression(fit_intercept=False)
model_after.fit(X_after, y_after)

y_pred_prior = pd.Series(model_prior.predict(X_prior), index=X_prior.index)
y_pred_after = pd.Series(model_after.predict(X_after), index=X_after.index)

y_pred_total = pd.concat([y_pred_prior, y_pred_after], axis=0)

print(y_pred_total.reset_index(drop=True).tail())

47    16937.640316
48    17478.371542
49    18019.102767
50    18559.833992
51    19100.565217
dtype: float64


X_prior.index

DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01'],
              dtype='datetime64[ns]', name='month_year', freq=None)


plt.figure(figsize=(12, 6), dpi=80)

plt.plot(X_prior.index, y_pred_prior, marker = 'o')
plt.plot(X_after.index, y_pred_after, marker = 'o')

plt.plot(top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = '+')
plt.plot(top_mkb_f.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = 'v')
plt.plot(top_mkb_m.groupby('month_year').sum('PATIENT_ID_COUNT'), marker = '^')

[plt.axvline([dt.datetime(year, 1, 1)]) for year in range(2018, 2023)]

plt.legend(['fit before corona', 'fit after corona', 'total', 'female', 'male'])

plt.grid()
plt.show()


import rsnd


fft_ = rsnd.fft_angle(top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT'), 1)


fft_

{'frequency': array([0.        , 0.01960784, 0.03921569, 0.05882353, 0.07843137,
        0.09803922, 0.11764706, 0.1372549 , 0.15686275, 0.17647059,
        0.19607843, 0.21568627, 0.23529412, 0.25490196, 0.2745098 ,
        0.29411765, 0.31372549, 0.33333333, 0.35294118, 0.37254902,
        0.39215686, 0.41176471, 0.43137255, 0.45098039, 0.47058824]),
 'fft_amplitude': array([[236.96078431],
        [621.37254902],
        [733.45098039],
        [609.29411765],
        [319.45098039],
        [232.2745098 ],
        [221.64705882],
        [283.60784314],
        [465.64705882],
        [546.74509804],
        [478.31372549],
        [497.80392157],
        [526.58823529],
        [887.29411765],
        [556.54901961],
        [431.29411765],
        [262.62745098],
        [216.31372549],
        [231.76470588],
        [244.31372549],
        [495.80392157],
        [522.43137255],
        [384.31372549],
        [454.43137255],
        [408.62745098]])}


plt.plot(1/fft_['frequency'], fft_['fft_amplitude'], marker='o')
plt.show()


1/fft_['frequency']

array([        inf, 51.        , 25.5       , 17.        , 12.75      ,
       10.2       ,  8.5       ,  7.28571429,  6.375     ,  5.66666667,
        5.1       ,  4.63636364,  4.25      ,  3.92307692,  3.64285714,
        3.4       ,  3.1875    ,  3.        ,  2.83333333,  2.68421053,
        2.55      ,  2.42857143,  2.31818182,  2.2173913 ,  2.125     ])


1/fft_['frequency'][11]

4.636363636363636


fft_['fft_amplitude']

array([[236.96078431],
       [621.37254902],
       [733.45098039],
       [609.29411765],
       [319.45098039],
       [232.2745098 ],
       [221.64705882],
       [283.60784314],
       [465.64705882],
       [546.74509804],
       [478.31372549],
       [497.80392157],
       [526.58823529],
       [887.29411765],
       [556.54901961],
       [431.29411765],
       [262.62745098],
       [216.31372549],
       [231.76470588],
       [244.31372549],
       [495.80392157],
       [522.43137255],
       [384.31372549],
       [454.43137255],
       [408.62745098]])


# @link https://stackoverflow.com/questions/27516849/how-to-convert-list-of-numpy-arrays-into-single-numpy-array
np.concatenate(fft_['fft_amplitude'], axis=0)

array([236.96078431, 621.37254902, 733.45098039, 609.29411765,
       319.45098039, 232.2745098 , 221.64705882, 283.60784314,
       465.64705882, 546.74509804, 478.31372549, 497.80392157,
       526.58823529, 887.29411765, 556.54901961, 431.29411765,
       262.62745098, 216.31372549, 231.76470588, 244.31372549,
       495.80392157, 522.43137255, 384.31372549, 454.43137255,
       408.62745098])


np.stack(fft_['fft_amplitude'], axis=0)
np.vstack(fft_['fft_amplitude'])
np.array(fft_['fft_amplitude'])

array([[236.96078431],
       [621.37254902],
       [733.45098039],
       [609.29411765],
       [319.45098039],
       [232.2745098 ],
       [221.64705882],
       [283.60784314],
       [465.64705882],
       [546.74509804],
       [478.31372549],
       [497.80392157],
       [526.58823529],
       [887.29411765],
       [556.54901961],
       [431.29411765],
       [262.62745098],
       [216.31372549],
       [231.76470588],
       [244.31372549],
       [495.80392157],
       [522.43137255],
       [384.31372549],
       [454.43137255],
       [408.62745098]])


s1 = pd.Series(1/fft_['frequency'])


s2 = pd.Series(np.concatenate(fft_['fft_amplitude'], axis=0))


f=pd.concat([s1, s2], axis = 1, names=['Period, month', 'Amplitude'])


plt.plot(f[0], f[1], marker='o')
plt.show()


y_p_t = pd.DataFrame(y_pred_total)


y_p_t.rename(columns={0: 'PATIENT_ID_COUNT'}, inplace=True)
y_p_t


top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT') - y_p_t


plt.figure(figsize=(12, 6), dpi=80)

plt.plot(top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT') - y_p_t, marker = '+')
plt.plot(top_mkb_f.groupby('month_year').sum('PATIENT_ID_COUNT') - y_p_t, marker = 'v')
plt.plot(top_mkb_m.groupby('month_year').sum('PATIENT_ID_COUNT') - y_p_t, marker = '^')

[plt.axvline([dt.datetime(year, 1, 1)]) for year in range(2018, 2023)]

plt.title('DETRENDED')

plt.legend(['total', 'female', 'male'])

plt.grid()
plt.show()


# @link https://www.kaggle.com/code/ryanholbrook/hybrid-models/tutorial


y_detrend = top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT') - y_p_t
y_detrend


from scipy import integrate

#y_detrend.apply(lambda g: integrate.trapz(g.PATIENT_ID_COUNT, x=g.month_year))


y_detrend[:52].sum()

PATIENT_ID_COUNT    3.637979e-11
dtype: float64


y_p_t


#model_after.predict(X)
#y_pred_after = pd.Series(model_after.predict(X_after), index=X_after.index)
X_after
X_after.index[-1:]
d = {'a': 1, 'b': 2, 'c': 3}
da = {dt.datetime(2022,4,1): 0}
ser = pd.Series(da, name='for pred')
#ser = pd.Series(data=d, index=['a', 'b', 'c'])
ser

2022-04-01    0
Name: for pred, dtype: int64

X


!pip install xgboost

Requirement already satisfied: xgboost in d:\programdata\anaconda3\lib\site-packages (1.5.2)

WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Error parsing requirements for jupyter-packaging: [Errno 2] No such file or directory: 'd:\\programdata\\anaconda3\\lib\\site-packages\\jupyter_packaging-0.7.12.dist-info\\METADATA'
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -yflakes (d:\programdata\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -ip (d:\programdata\anaconda3\lib\site-packages)

Requirement already satisfied: scipy in d:\programdata\anaconda3\lib\site-packages (from xgboost) (1.7.3)
Requirement already satisfied: numpy in d:\programdata\anaconda3\lib\site-packages (from xgboost) (1.20.3)


from xgboost import XGBRegressor


# Pivot wide to long (stack) and convert DataFrame to Series (squeeze)
#y_fit = y_fit.stack().squeeze()    # trend from training set
#y_pred = y_pred.stack().squeeze()  # trend from test set

# Create residuals (the collection of detrended series) from the training set
#y_resid = y_train - y_fit

# Train XGBoost on the residuals
xgb = XGBRegressor()
xgb.fit(X_after, y_detrend[corona_turn_idx:])

# Add the predicted residuals onto the predicted trends
#y_fit_boosted = xgb.predict(X_train) + y_fit
#y_pred_boosted = xgb.predict(X_test) + y_pred

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,
             gamma=0, gpu_id=-1, importance_type=None,
             interaction_constraints='', learning_rate=0.300000012,
             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100, n_jobs=8,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',
             validate_parameters=1, verbosity=None)


X_after


xgb_sh_1 = XGBRegressor()
xgb_sh_1.fit(X_after[:-1], y_detrend.iloc[corona_turn_idx:-1])

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,
             gamma=0, gpu_id=-1, importance_type=None,
             interaction_constraints='', learning_rate=0.300000012,
             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100, n_jobs=8,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',
             validate_parameters=1, verbosity=None)


X_after[-1:]


y_p_t[-1:]


xgb_sh_1.predict(X_after[-1:])

array([2422.165], dtype=float32)


y_p_t[-1:]


xgb_sh_1.predict(X_after[-1:]) + y_p_t[-1:]


X_test = pd.DataFrame(data = {'PATIENT_ID_COUNT': [0]}, index=[dt.datetime(2022,4,1)])
X_test


xgb.predict(dp_after.out_of_sample(1))

array([-10511.369], dtype=float32)


# @ 2022-04-01
xgb.predict(dp_after.out_of_sample(1))+model_after.predict(dp_after.out_of_sample(1))

array([9129.92730206])


X_test.index

DatetimeIndex(['2022-04-01'], dtype='datetime64[ns]', freq=None)


X_after


# Add the predicted residuals onto the predicted trends
y_fit_boosted = xgb.predict(X_train) + y_fit
y_pred_boosted = xgb.predict(X_test) + y_pred


# source: https://www.kaggle.com/code/ryanholbrook/seasonality/tutorial


from pathlib import Path
from warnings import simplefilter

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
from statsmodels.tsa.deterministic import CalendarFourier, DeterministicProcess

simplefilter("ignore")

# Set Matplotlib defaults
plt.style.use("seaborn-whitegrid")
plt.rc("figure", autolayout=True, figsize=(11, 5))
plt.rc(
    "axes",
    labelweight="bold",
    labelsize="large",
    titleweight="bold",
    titlesize=16,
    titlepad=10,
)
plot_params = dict(
    color="0.75",
    style=".-",
    markeredgecolor="0.25",
    markerfacecolor="0.25",
    legend=False,
)
%config InlineBackend.figure_format = 'retina'


# annotations: https://stackoverflow.com/a/49238256/5769929
def seasonal_plot(X, y, period, freq, ax=None):
    if ax is None:
        _, ax = plt.subplots()
    palette = sns.color_palette("husl", n_colors=X[period].nunique(),)
    ax = sns.lineplot(
        x=freq,
        y=y,
        hue=period,
        data=X,
        ci=False,
        ax=ax,
        palette=palette,
        legend=False,
    )
    ax.set_title(f"Seasonal Plot ({period}/{freq})")
    for line, name in zip(ax.lines, X[period].unique()):
        y_ = line.get_ydata()[-1]
        ax.annotate(
            name,
            xy=(1, y_),
            xytext=(6, 0),
            color=line.get_color(),
            xycoords=ax.get_yaxis_transform(),
            textcoords="offset points",
            size=14,
            va="center",
        )
    return ax


def plot_periodogram(ts, detrend='linear', ax=None):
    from scipy.signal import periodogram
    fs = pd.Timedelta("1Y") / pd.Timedelta(np.timedelta64(1, "M"))
    freqencies, spectrum = periodogram(
        ts,
        fs=fs,
        detrend=detrend,
        window="boxcar",
        scaling='spectrum',
    )
    if ax is None:
        _, ax = plt.subplots()
    ax.step(freqencies, spectrum, color="purple")
    ax.set_xscale("log")
    #ax.set_xticks([1, 2, 4, 6, 12, 26, 52, 104])
    ax.set_xticks([1, 2, 4, 6])
    ax.set_xticklabels(
        [
            "Annual (1)",
            "Semiannual (2)",
            "Quarterly (4)",
            "Bimonthly (6)",
            #"Monthly (12)",
            #"Biweekly (26)",
            #"Weekly (52)",
            #"Semiweekly (104)",
        ],
        rotation=30,
    )
    ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
    ax.set_ylabel("Variance")
    ax.set_title("Periodogram")
    return ax


#data_dir = Path("../input/ts-course-data")
#tunnel = pd.read_csv(data_dir / "tunnel.csv", parse_dates=["Day"])
#tunnel = tunnel.set_index("Day").to_period("D")


X_n = top_mkb.groupby('month_year').sum('PATIENT_ID_COUNT').copy()

# days within a week
X_n["day"] = X_n.index.dayofweek  # the x-axis (freq)
X_n["week"] = X_n.index.week  # the seasonal period (period)

# days within a year
X_n["month"] = X_n.index.month
X_n["year"] = X_n.index.year

fig, ax0 = plt.subplots(1, 1, figsize=(11, 6))

#seasonal_plot(X_n, y="PATIENT_ID_COUNT", period="week", freq="day", ax=ax0)
#seasonal_plot(X_n, y="PATIENT_ID_COUNT", period="year", freq="month", ax=ax1);
seasonal_plot(X_n, y="PATIENT_ID_COUNT", period="year", freq="month", ax=ax0);


X_n.head()


type(X_n.PATIENT_ID_COUNT.head())

pandas.core.series.Series


plot_periodogram(X_n.PATIENT_ID_COUNT);


pd.DatetimeIndex(X_n.index)

DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',
               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',
               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',
               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',
               '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',
               '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01',
               '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01',
               '2022-01-01', '2022-02-01', '2022-03-01'],
              dtype='datetime64[ns]', name='month_year', freq=None)


from statsmodels.tsa.deterministic import CalendarFourier, DeterministicProcess

fourier = CalendarFourier(freq="A", order=4)  # 10 sin/cos pairs for "A"nnual seasonality

dp_s = DeterministicProcess(
    #index=X_n.index,
    index=pd.DatetimeIndex(X_n.index),
    constant=True,               # dummy feature for bias (y-intercept)
    order=1,                     # trend (order 1 means linear)
    seasonal=True,               # weekly seasonality (indicators)
    additional_terms=[fourier],  # annual seasonality (fourier)
    drop=True,                   # drop terms to avoid collinearity
)

X_s = dp_s.in_sample()  # create features for dates in tunnel.index

X_s


dp_s.out_of_sample(steps=51,forecast_index=X_n.index)


X_n.index+51*np.timedelta64(1, 'M')

DatetimeIndex(['2022-04-02 06:44:06', '2022-05-03 06:44:06',
               '2022-05-31 06:44:06', '2022-07-01 06:44:06',
               '2022-07-31 06:44:06', '2022-08-31 06:44:06',
               '2022-09-30 06:44:06', '2022-10-31 06:44:06',
               '2022-12-01 06:44:06', '2022-12-31 06:44:06',
               '2023-01-31 06:44:06', '2023-03-02 06:44:06',
               '2023-04-02 06:44:06', '2023-05-03 06:44:06',
               '2023-05-31 06:44:06', '2023-07-01 06:44:06',
               '2023-07-31 06:44:06', '2023-08-31 06:44:06',
               '2023-09-30 06:44:06', '2023-10-31 06:44:06',
               '2023-12-01 06:44:06', '2023-12-31 06:44:06',
               '2024-01-31 06:44:06', '2024-03-01 06:44:06',
               '2024-04-01 06:44:06', '2024-05-02 06:44:06',
               '2024-05-31 06:44:06', '2024-07-01 06:44:06',
               '2024-07-31 06:44:06', '2024-08-31 06:44:06',
               '2024-09-30 06:44:06', '2024-10-31 06:44:06',
               '2024-12-01 06:44:06', '2024-12-31 06:44:06',
               '2025-01-31 06:44:06', '2025-03-02 06:44:06',
               '2025-04-02 06:44:06', '2025-05-03 06:44:06',
               '2025-05-31 06:44:06', '2025-07-01 06:44:06',
               '2025-07-31 06:44:06', '2025-08-31 06:44:06',
               '2025-09-30 06:44:06', '2025-10-31 06:44:06',
               '2025-12-01 06:44:06', '2025-12-31 06:44:06',
               '2026-01-31 06:44:06', '2026-03-02 06:44:06',
               '2026-04-02 06:44:06', '2026-05-03 06:44:06',
               '2026-05-31 06:44:06'],
              dtype='datetime64[ns]', name='month_year', freq=None)


pd.date_range(start='2022-04-01', end='2026-07-01', freq='M').shape

(51,)


X_n['PATIENT_ID_COUNT'].tail()

month_year
2021-11-01    13441
2021-12-01    27815
2022-01-01    18372
2022-02-01    20982
2022-03-01     8589
Name: PATIENT_ID_COUNT, dtype: int32


X_n.index[-1]

Timestamp('2022-03-01 00:00:00')


y_s = X_n['PATIENT_ID_COUNT']

model = LinearRegression(fit_intercept=False)
_ = model.fit(X_s, y_s)

y_pred = pd.Series(model.predict(X_s), index=y_s.index)
#X_fore = dp_s.out_of_sample(steps=51, forecast_index=X_n.index+51*np.timedelta64(1, 'M'))
#X_fore = dp_s.out_of_sample(steps=51, forecast_index=pd.date_range(start='2022-04-01', end='2026-07-01', freq='M'))

#y_fore = pd.Series(model.predict(X_fore), index=X_fore.index)

ax = y_s.plot(color='0.25', style='.', title="TOP MKB - Seasonal Forecast")
ax = y_pred.plot(ax=ax, label="Seasonal")
#ax = y_fore.plot(ax=ax, label="Seasonal Forecast", color='C3')
#ax.set_xlim(X_n.index[0], X_n.index[-1])
_ = ax.legend()


# Correlations https://www.kaggle.com/code/ryanholbrook/time-series-as-features/tutorial


from pathlib import Path
from warnings import simplefilter

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.signal import periodogram
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from statsmodels.graphics.tsaplots import plot_pacf

simplefilter("ignore")

# Set Matplotlib defaults
plt.style.use("seaborn-whitegrid")
plt.rc("figure", autolayout=True, figsize=(11, 4))
plt.rc(
    "axes",
    labelweight="bold",
    labelsize="large",
    titleweight="bold",
    titlesize=16,
    titlepad=10,
)
plot_params = dict(
    color="0.75",
    style=".-",
    markeredgecolor="0.25",
    markerfacecolor="0.25",
)
%config InlineBackend.figure_format = 'retina'


def lagplot(x, y=None, lag=1, standardize=False, ax=None, **kwargs):
    from matplotlib.offsetbox import AnchoredText
    x_ = x.shift(lag)
    if standardize:
        x_ = (x_ - x_.mean()) / x_.std()
    if y is not None:
        y_ = (y - y.mean()) / y.std() if standardize else y
    else:
        y_ = x
    corr = y_.corr(x_)
    if ax is None:
        fig, ax = plt.subplots()
    scatter_kws = dict(
        alpha=0.75,
        s=3,
    )
    line_kws = dict(color='C3', )
    ax = sns.regplot(x=x_,
                     y=y_,
                     scatter_kws=scatter_kws,
                     line_kws=line_kws,
                     lowess=True,
                     ax=ax,
                     **kwargs)
    at = AnchoredText(
        f"{corr:.2f}",
        prop=dict(size="large"),
        frameon=True,
        loc="upper left",
    )
    at.patch.set_boxstyle("square, pad=0.0")
    ax.add_artist(at)
    ax.set(title=f"Lag {lag}", xlabel=x_.name, ylabel=y_.name)
    return ax


def plot_lags(x, y=None, lags=6, nrows=1, lagplot_kwargs={}, **kwargs):
    import math
    kwargs.setdefault('nrows', nrows)
    kwargs.setdefault('ncols', math.ceil(lags / nrows))
    kwargs.setdefault('figsize', (kwargs['ncols'] * 2, nrows * 2 + 0.5))
    fig, axs = plt.subplots(sharex=True, sharey=True, squeeze=False, **kwargs)
    for ax, k in zip(fig.get_axes(), range(kwargs['nrows'] * kwargs['ncols'])):
        if k + 1 <= lags:
            ax = lagplot(x, y, lag=k + 1, ax=ax, **lagplot_kwargs)
            ax.set_title(f"Lag {k + 1}", fontdict=dict(fontsize=14))
            ax.set(xlabel="", ylabel="")
        else:
            ax.axis('off')
    plt.setp(axs[-1, :], xlabel=x.name)
    plt.setp(axs[:, 0], ylabel=y.name if y is not None else x.name)
    fig.tight_layout(w_pad=0.1, h_pad=0.1)
    return fig


#data_dir = Path("../input/ts-course-data")
#flu_trends = pd.read_csv(data_dir / "flu-trends.csv")
#flu_trends.set_index(
#    pd.PeriodIndex(flu_trends.Week, freq="W"),
#    inplace=True,
#)
#flu_trends.drop("Week", axis=1, inplace=True)

#ax = flu_trends.FluVisits.plot(title='Flu Trends', **plot_params)
#_ = ax.set(ylabel="Office Visits")


flu_trends = X_n.copy()
flu_trends.head()


flu_trends.

Index(['PATIENT_ID_COUNT', 'day', 'week', 'month', 'year'], dtype='object')


flu_trends.set_index(pd.PeriodIndex(flu_trends.index, freq="M"), inplace=True)


ax = flu_trends.PATIENT_ID_COUNT.plot(title='Flu Trends', **plot_params)
_ = ax.set(ylabel="Office Visits")


# Import Meteostat library and dependencies
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Point, Daily, Monthly

# Set time period
start = datetime(2018, 1, 1)
end = datetime(2022, 4, 30)

# Create Point for Kaliningrad
location = Point(54.710128, 20.5105838, 0)

# Get daily data for 2018
data = Monthly(location, start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'], marker='o')
plt.show()


flu_trends.shape

(51, 5)


data


data.shape

(52, 7)


# Correlation of flu trends with temperature weather


flu_trends.PATIENT_ID_COUNT.pct_change().plot()

<AxesSubplot:xlabel='month_year'>


flu_trends.PATIENT_ID_COUNT.pct_change()[1:].corr(data.tavg[:-1].pct_change()[1:])

nan


for sh in range(12):
    cor1 = pd.Series(flu_trends.PATIENT_ID_COUNT.pct_change()[1:]).reset_index(drop=True).shift(-sh).fillna(0)
    cor2 = pd.Series(data.tavg[:-1].pct_change()[1:]).reset_index(drop=True)
    print(cor1.corr(cor2))

-0.06417580979741487
0.02250092114205408
0.14118144584977046
0.22176692198809464
0.12883386102187286
0.019168390533101202
-0.060192140185869866
-0.1621198229272845
-0.03493780901316206
0.03750022489283038
-0.04497970898513469
0.02043814618039486


for sh in range(12):
    cor1 = pd.Series(flu_trends.PATIENT_ID_COUNT.diff()[1:]).reset_index(drop=True).shift(sh).fillna(0)
    cor2 = pd.Series(data.tavg[:-1].diff()[1:]).reset_index(drop=True)
    print(cor1.corr(cor2))

-0.2941965409111408
-0.16278038156263508
-0.3144270207615394
-0.1985883753370914
0.19419555431362068
0.08239732298087364
0.2955482434617382
0.2543930268057727
0.28567833315018476
0.10404052903111528
-0.07941739892006597
-0.10885793969789104


for sh in range(12):
    cor = pd.Series(data.tavg).reset_index(drop=True).corr(pd.Series(flu_trends.PATIENT_ID_COUNT).reset_index(drop=True).shift(-sh).fillna(0))
    print(cor)

-0.538324561245258
-0.2678961701573419
0.06754337664020182
0.4180993072467921
0.6149283850857821
0.6434223700554677
0.5356218527096469
0.279249588312017
0.0004965990130966684
-0.21641064847307917
-0.38776846208139465
-0.4382146984850754


pd.Series(data.tavg).reset_index(drop=True).diff().plot(marker='o')

<AxesSubplot:>


ft_moving_average = flu_trends.PATIENT_ID_COUNT.rolling(
    window=8,       # 365-day window
    center=True,      # puts the average at the center of the window
    min_periods=4,  # choose about half the window size
).mean()              # compute the mean (could also do median, std, min, max, ...)


ft_moving_average.plot(marker='o')

<AxesSubplot:xlabel='month_year'>


data.tavg[:-1].pct_change()[1:]

time
2018-02-01   -44.000000
2018-03-01    -0.813953
2018-04-01   -13.875000
2018-05-01     0.543689
2018-06-01     0.081761
2018-07-01     0.180233
2018-08-01    -0.034483
2018-09-01    -0.219388
2018-10-01    -0.326797
2018-11-01    -0.563107
2018-12-01    -0.577778
2019-01-01    -1.736842
2019-02-01    -3.142857
2019-03-01     0.566667
2019-04-01     0.787234
2019-05-01     0.416667
2019-06-01     0.689076
2019-07-01    -0.124378
2019-08-01     0.056818
2019-09-01    -0.241935
2019-10-01    -0.241135
2019-11-01    -0.439252
2019-12-01    -0.316667
2020-01-01     0.097561
2020-02-01    -0.066667
2020-03-01     0.095238
2020-04-01     0.630435
2020-05-01     0.386667
2020-06-01     0.721154
2020-07-01    -0.027933
2020-08-01     0.091954
2020-09-01    -0.173684
2020-10-01    -0.292994
2020-11-01    -0.351351
2020-12-01    -0.694444
2021-01-01    -1.590909
2021-02-01     1.307692
2021-03-01    -2.100000
2021-04-01     0.818182
2021-05-01     0.933333
2021-06-01     0.612069
2021-07-01     0.160428
2021-08-01    -0.216590
2021-09-01    -0.205882
2021-10-01    -0.296296
2021-11-01    -0.389474
2021-12-01    -1.137931
2022-01-01    -3.750000
2022-02-01     0.500000
2022-03-01    -0.303030
Freq: MS, Name: tavg, dtype: float64


flu_trends.PATIENT_ID_COUNT.pct_change()

month_year
2018-01         NaN
2018-02    0.311129
2018-03    0.180372
2018-04   -0.169278
2018-05   -0.475703
2018-06   -0.272895
2018-07   -0.045754
2018-08    0.279547
2018-09    0.641869
2018-10    0.174162
2018-11   -0.125161
2018-12    0.040748
2019-01    0.057823
2019-02    0.684987
2019-03   -0.372757
2019-04   -0.225056
2019-05   -0.391071
2019-06   -0.176348
2019-07    0.071429
2019-08    0.054146
2019-09    1.029374
2019-10    0.053706
2019-11   -0.264375
2019-12    0.182449
2020-01   -0.100794
2020-02    0.658253
2020-03   -0.033914
2020-04   -0.671359
2020-05   -0.564710
2020-06    0.036013
2020-07    1.300323
2020-08   -0.042172
2020-09    1.073381
2020-10    0.407362
2020-11    0.386395
2020-12    0.038727
2021-01   -0.574522
2021-02    0.139031
2021-03   -0.019007
2021-04   -0.024977
2021-05   -0.192315
2021-06    0.018424
2021-07    0.149531
2021-08    0.103486
2021-09    0.899540
2021-10    0.107627
2021-11   -0.423381
2021-12    1.069414
2022-01   -0.339493
2022-02    0.142064
2022-03   -0.590649
Freq: M, Name: PATIENT_ID_COUNT, dtype: float64


pd.Series(list(range(10))).corr(pd.Series(list(range(10))))

0.9999999999999999


pd.Series(data.tavg[:-1].pct_change())[1:]

time
2018-02-01   -44.000000
2018-03-01    -0.813953
2018-04-01   -13.875000
2018-05-01     0.543689
2018-06-01     0.081761
2018-07-01     0.180233
2018-08-01    -0.034483
2018-09-01    -0.219388
2018-10-01    -0.326797
2018-11-01    -0.563107
2018-12-01    -0.577778
2019-01-01    -1.736842
2019-02-01    -3.142857
2019-03-01     0.566667
2019-04-01     0.787234
2019-05-01     0.416667
2019-06-01     0.689076
2019-07-01    -0.124378
2019-08-01     0.056818
2019-09-01    -0.241935
2019-10-01    -0.241135
2019-11-01    -0.439252
2019-12-01    -0.316667
2020-01-01     0.097561
2020-02-01    -0.066667
2020-03-01     0.095238
2020-04-01     0.630435
2020-05-01     0.386667
2020-06-01     0.721154
2020-07-01    -0.027933
2020-08-01     0.091954
2020-09-01    -0.173684
2020-10-01    -0.292994
2020-11-01    -0.351351
2020-12-01    -0.694444
2021-01-01    -1.590909
2021-02-01     1.307692
2021-03-01    -2.100000
2021-04-01     0.818182
2021-05-01     0.933333
2021-06-01     0.612069
2021-07-01     0.160428
2021-08-01    -0.216590
2021-09-01    -0.205882
2021-10-01    -0.296296
2021-11-01    -0.389474
2021-12-01    -1.137931
2022-01-01    -3.750000
2022-02-01     0.500000
2022-03-01    -0.303030
Freq: MS, Name: tavg, dtype: float64


data.tavg[:-1].pct_change().plot()

<AxesSubplot:xlabel='time'>


plt.scatter(flu_trends.PATIENT_ID_COUNT, data.tavg[:-1])

<matplotlib.collections.PathCollection at 0x2043f915b50>


_ = plot_lags(flu_trends.PATIENT_ID_COUNT, lags=12, nrows=2)
_ = plot_pacf(flu_trends.PATIENT_ID_COUNT, lags=12)


_ = plot_lags(flu_trends.PATIENT_ID_COUNT[-36:], lags=12, nrows=2)
_ = plot_pacf(flu_trends.PATIENT_ID_COUNT[-36:], lags=12)


#ACF
from statsmodels.graphics.tsaplots import plot_acf
plot_acf(flu_trends.PATIENT_ID_COUNT, lags=12)


# https://medium.com/@krzysztofdrelczuk/acf-autocorrelation-function-simple-explanation-with-python-example-492484c32711
# https://github.com/kdrelczuk/medium
from pandas import read_csv
from statsmodels.graphics.tsaplots import plot_acf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

data = pd.read_csv('monthly-sunspots.txt').drop(['Month'],axis=1)#.head(100)
data_a = data.to_numpy().T[0]
data_a
plt.figure(figsize=(20,10))
plt.plot(data_a)

plt.rc("figure", figsize=(20,10))
plt.figure(figsize=(20,10))
plot_acf(data_a, lags=90)
plt.show()

data = pd.read_csv('AirPassengers.csv').drop(['Month'],axis=1)#.head(100)
data_a = data.to_numpy().T[0]
data_a
plt.figure(figsize=(20,10))
plt.plot(data_a)

plt.rc("figure", figsize=(20,10))
plt.figure(figsize=(20,10))
plot_acf(data_a, lags=50)
plt.show()

<Figure size 1440x720 with 0 Axes>

<Figure size 1440x720 with 0 Axes>


_ = plot_lags(data.tavg, lags=12, nrows=2)
_ = plot_pacf(data.tavg, lags=12)


data.tavg

time
2018-01-01     0.1
2018-02-01    -4.3
2018-03-01    -0.8
2018-04-01    10.3
2018-05-01    15.9
2018-06-01    17.2
2018-07-01    20.3
2018-08-01    19.6
2018-09-01    15.3
2018-10-01    10.3
2018-11-01     4.5
2018-12-01     1.9
2019-01-01    -1.4
2019-02-01     3.0
2019-03-01     4.7
2019-04-01     8.4
2019-05-01    11.9
2019-06-01    20.1
2019-07-01    17.6
2019-08-01    18.6
2019-09-01    14.1
2019-10-01    10.7
2019-11-01     6.0
2019-12-01     4.1
2020-01-01     4.5
2020-02-01     4.2
2020-03-01     4.6
2020-04-01     7.5
2020-05-01    10.4
2020-06-01    17.9
2020-07-01    17.4
2020-08-01    19.0
2020-09-01    15.7
2020-10-01    11.1
2020-11-01     7.2
2020-12-01     2.2
2021-01-01    -1.3
2021-02-01    -3.0
2021-03-01     3.3
2021-04-01     6.0
2021-05-01    11.6
2021-06-01    18.7
2021-07-01    21.7
2021-08-01    17.0
2021-09-01    13.5
2021-10-01     9.5
2021-11-01     5.8
2021-12-01    -0.8
2022-01-01     2.2
2022-02-01     3.3
2022-03-01     2.3
2022-04-01     6.6
Freq: MS, Name: tavg, dtype: float64


def make_lags(ts, lags):
    return pd.concat(
        {
            f'y_lag_{i}': ts.shift(i)
            for i in range(1, lags + 1)
        },
        axis=1)


X = make_lags(flu_trends.PATIENT_ID_COUNT, lags=1)
X = X.fillna(0.0)


# Create target series and data splits
y = flu_trends.PATIENT_ID_COUNT.copy()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=10, shuffle=False)

# Fit and predict
model = LinearRegression()  # `fit_intercept=True` since we didn't use DeterministicProcess
model.fit(X_train, y_train)
y_pred = pd.Series(model.predict(X_train), index=y_train.index)
y_fore = pd.Series(model.predict(X_test), index=y_test.index)


ax = y_train.plot(**plot_params)
ax = y_test.plot(**plot_params)
ax = y_pred.plot(ax=ax)
_ = y_fore.plot(ax=ax, color='C3')


ax = y_test.plot(**plot_params)
_ = y_fore.plot(ax=ax, color='C3')


# Import Meteostat library and dependencies
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Point, Daily

# Set time period
start = datetime(2018, 1, 1)
end = datetime(2022, 4, 30)

# Create Point for Kaliningrad
location = Point(54.710128, 20.5105838, 0)

# Get daily data for 2018
data = Daily(location, start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()


# Import Meteostat library and dependencies
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Stations, Monthly

# Set time period
start = datetime(2000, 1, 1)
end = datetime(2018, 12, 31)

# Get Monthly data
data = Monthly('10637', start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()


_ = plot_lags(flu_trends.FluVisits, lags=12, nrows=2)
_ = plot_pacf(flu_trends.FluVisits, lags=12)


train.groupby('MKB_CODE').sum('PATIENT_ID_COUNT').describe()


train.groupby('VISIT_MONTH_YEAR').sum('PATIENT_ID_COUNT').describe()


grp_by_month = train
grp_by_month['month'] = pd.to_datetime(grp_by_month['VISIT_MONTH_YEAR'], format='%m.%y', errors='ignore')
grp_by_month = grp_by_month.groupby('month').sum('PATIENT_ID_COUNT')
grp_by_month.describe()


grp_by_month


plt.plot(grp_by_month['PATIENT_ID_COUNT'], marker = 'o')
plt.rcParams["figure.figsize"] = (20,30)
plt.show()


!pip install geopy

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!pip install geopy
!pip install geocoder

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Collecting geocoder
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Installing collected packages: ratelim, geocoder
Successfully installed geocoder-1.38.1 ratelim-0.1.6


from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="my-my-application")
location = geolocator.geocode("Балтийск Россия")
print(location.address)
#Flatiron Building, 175, 5th Avenue, Flatiron, New York, NYC, New York, ...
print((location.latitude, location.longitude))
#(40.7410861, -73.9896297241625)

Балтийск, Балтийский городской округ, Калининградская область, Северо-Западный федеральный округ, Россия
(54.6437214, 19.8941584)


from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="my-my-application")
location = geolocator.geocode("Калининград Россия")
print(location.address)
#Flatiron Building, 175, 5th Avenue, Flatiron, New York, NYC, New York, ...
print((location.latitude, location.longitude))
#(40.7410861, -73.9896297241625)

Калининград, городской округ Калининград, Калининградская область, Северо-Западный федеральный округ, Россия
(54.710128, 20.5105838)


def city_state_country(row):
    coord = f"{row['Latitude']}, {row['Longitude']}"
    location = geolocator.reverse(coord, exactly_one=True)
    address = location.raw['address']
    city = address.get('city', '')
    state = address.get('state', '')
    country = address.get('country', '')
    row['city'] = city
    row['state'] = state
    row['country'] = country
    return row


row = {'Latitude': 54.64, 'Longitude': 19.89}
row = {'Latitude': 55.644466, 'Longitude': 37.395744}
city_state_country(row)

{'Latitude': 55.644466,
 'Longitude': 37.395744,
 'city': 'Москва',
 'state': 'Москва',
 'country': 'Россия'}


import geocoder

g = geocoder.osm([51.5074, 0.1278], method='reverse')
g.json['city']

'London'


import geocoder

g = geocoder.osm([54.6437214, 19.8941584], method='reverse')
#str(g).split(',').strip()
print(g.json['town'])
g.json

Балтийск

{'accuracy': 0.001,
 'address': 'Администрация Балтийского муниципального района, 6, проспект Ленина, Балтийск, Балтийский городской округ, Калининградская область, Северо-Западный федеральный округ, 238520, Россия',
 'bbox': {'northeast': [54.6435977, 19.894209],
  'southwest': [54.6429427, 19.8938801]},
 'confidence': 10,
 'country': 'Россия',
 'country_code': 'ru',
 'county': 'Балтийский городской округ',
 'housenumber': '6',
 'importance': 0.001,
 'lat': 54.6431757,
 'lng': 19.8940599350598,
 'ok': True,
 'osm_id': 170844680,
 'osm_type': 'way',
 'place_id': 145593510,
 'place_rank': 30,
 'postal': '238520',
 'quality': 'townhall',
 'raw': {'place_id': 145593510,
  'licence': 'Data © OpenStreetMap contributors, ODbL 1.0. https://osm.org/copyright',
  'osm_type': 'way',
  'osm_id': 170844680,
  'boundingbox': ['54.6429427', '54.6435977', '19.8938801', '19.894209'],
  'lat': '54.6431757',
  'lon': '19.8940599350598',
  'display_name': 'Администрация Балтийского муниципального района, 6, проспект Ленина, Балтийск, Балтийский городской округ, Калининградская область, Северо-Западный федеральный округ, 238520, Россия',
  'place_rank': 30,
  'category': 'amenity',
  'type': 'townhall',
  'importance': 0.001,
  'address': {'amenity': 'Администрация Балтийского муниципального района',
   'house_number': '6',
   'road': 'проспект Ленина',
   'town': 'Балтийск',
   'county': 'Балтийский городской округ',
   'state': 'Калининградская область',
   'ISO3166-2-lvl4': 'RU-KGD',
   'region': 'Северо-Западный федеральный округ',
   'postcode': '238520',
   'country': 'Россия',
   'country_code': 'ru'}},
 'region': 'Калининградская область',
 'state': 'Калининградская область',
 'status': 'OK',
 'street': 'проспект Ленина',
 'town': 'Балтийск',
 'type': 'townhall'}


!pip install meteostat

Collecting meteostat

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  Downloading meteostat-1.6.4-py3-none-any.whl (31 kB)
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Installing collected packages: meteostat
Successfully installed meteostat-1.6.4


# Import Meteostat library and dependencies
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Point, Daily

# Set time period
start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31)

# Create Point for Vancouver, BC
location = Point(49.2497, -123.1193, 70)

# Get daily data for 2018
data = Daily(location, start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()


# Import Meteostat library and dependencies
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Point, Daily

# Set time period
start = datetime(2018, 1, 1)
end = datetime(2022, 4, 30)

# Create Point for Kaliningrad
location = Point(54.710128, 20.5105838, 0)

# Get daily data for 2018
data = Daily(location, start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()


import requests
import json

def get_city_opendata(city, country):
    tmp = 'https://public.opendatasoft.com/api/records/1.0/search/?dataset=worldcitiespop&q=%s&sort=population&facet=country&refine.country=%s'
    cmd = tmp % (city, country)
    res = requests.get(cmd)
    dct = json.loads(res.content)
    return dct

#    out = dct['records'][0]['fields']
#    return out

get_city_opendata('Berlin', 'de')

#{'city': 'berlin',
# 'country': 'de',
# 'region': '16',
# 'geopoint': [52.516667, 13.4],
# 'longitude': 13.4,
# 'latitude': 52.516667,
# 'accentcity': 'Berlin',
# 'population': 3398362}

get_city_opendata('San Francisco', 'us')

#{'city': 'san francisco',
# 'country': 'us',
# 'region': 'CA',
# 'geopoint': [37.775, -122.4183333],
# 'longitude': -122.4183333,
# 'latitude': 37.775,
# 'accentcity': 'San Francisco',
# 'population': 732072}

{'error': 'Unknown dataset: worldcitiespop'}


!pip install qwikidata

Collecting qwikidata

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  Downloading qwikidata-0.4.1-py3-none-any.whl (24 kB)
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Installing collected packages: qwikidata
Successfully installed qwikidata-0.4.1


import qwikidata
import qwikidata.sparql

def get_city_wikidata(city, country):
    query = """
    SELECT ?city ?cityLabel ?country ?countryLabel ?population
    WHERE
    {
      ?city rdfs:label '%s'@en.
      ?city wdt:P1082 ?population.
      ?city wdt:P17 ?country.
      ?city rdfs:label ?cityLabel.
      ?country rdfs:label ?countryLabel.
      FILTER(LANG(?cityLabel) = "en").
      FILTER(LANG(?countryLabel) = "en").
      FILTER(CONTAINS(?countryLabel, "%s")).
    }
    """ % (city, country)

    res = qwikidata.sparql.return_sparql_query_results(query)
    out = res['results']['bindings'][0]
    return out

get_city_wikidata('Berlin', 'Germany')

#{'city': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q64'},
# 'population': {'datatype': 'http://www.w3.org/2001/XMLSchema#decimal',
#  'type': 'literal',
#  'value': '3613495'},
# 'country': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q183'},
# 'cityLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Berlin'},
# 'countryLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Germany'}}

get_city_wikidata('San Francisco', 'America')

#{'city': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q62'},
# 'population': {'datatype': 'http://www.w3.org/2001/XMLSchema#decimal',
#  'type': 'literal',
#  'value': '805235'},
# 'country': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q30'},
# 'cityLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'San Francisco'},
# 'countryLabel': {'xml:lang': 'en',
#  'type': 'literal',
#  'value': 'United States of America'}}



get_city_wikidata('Baltiysk', 'Russia')

{'city': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q5660'},
 'population': {'datatype': 'http://www.w3.org/2001/XMLSchema#decimal',
  'type': 'literal',
  'value': '33317'},
 'country': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q159'},
 'cityLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Baltiysk'},
 'countryLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Russia'}}


get_city_wikidata('Kaliningrad', 'Russia')

{'city': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q1829'},
 'population': {'datatype': 'http://www.w3.org/2001/XMLSchema#decimal',
  'type': 'literal',
  'value': '475056'},
 'country': {'type': 'uri', 'value': 'http://www.wikidata.org/entity/Q159'},
 'cityLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Kaliningrad'},
 'countryLabel': {'xml:lang': 'en', 'type': 'literal', 'value': 'Russia'}}


import matplotlib.pyplot as plt


plt.plot(df_uniq_months.sort_values(ascending=True), marker='.')
plt.show()


#Разделение на train/test для локального тестирования

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)


#Создание объекта данных Pool, плюсы: возможность указать какие признаки являются категориальными

pool_train = Pool(X_train, y_train, cat_features = ['PATIENT_SEX', 'MKB_CODE', 'ADRES', 'VISIT_MONTH_YEAR', 'AGE_CATEGORY'])
pool_test = Pool(X_test, cat_features = ['PATIENT_SEX', 'MKB_CODE', 'ADRES', 'VISIT_MONTH_YEAR', 'AGE_CATEGORY'])


#Объявление CatBoostRegressor и обучение

#model = CatBoostRegressor(task_type='GPU')
model = CatBoostRegressor(task_type='CPU')
model.fit(pool_train)

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<catboost.core.CatBoostRegressor at 0x26a060a6fd0>


#Получение ответов модели на тестовой выборке в локальном тестировании 

y_pred = model.predict(pool_test)


#На локальном тестировании модель выдаёт такой результат

print("Значение метрики R2 на test: ", r2_score(y_test, y_pred))

Значение метрики R2 на test:  0.8682238751045731


#Формируем sample_solution. В обучении используется весь train, ответы получаем на test

pool_train_solution = Pool(X, y, cat_features = ['PATIENT_SEX', 'MKB_CODE', 'ADRES', 'VISIT_MONTH_YEAR', 'AGE_CATEGORY'])
pool_test_solution = Pool(test, cat_features = ['PATIENT_SEX', 'MKB_CODE', 'ADRES', 'VISIT_MONTH_YEAR', 'AGE_CATEGORY'])

#model_solution = CatBoostRegressor(task_type='GPU')
model_solution = CatBoostRegressor()
model_solution.fit(pool_train_solution)

Learning rate set to 0.138251
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<catboost.core.CatBoostRegressor at 0x26a0608daf0>


#Получение ответов

y_pred_solution = model.predict(pool_test_solution)


#Вот так они выглядят

y_pred_solution.astype(int)

array([2, 2, 2, ..., 6, 5, 5])


#Формируем sample_solution для отправки на платформу

test['PATIENT_ID_COUNT'] = y_pred_solution.astype(int)


#Сохраняем в csv файл
 
test.to_csv('sample_solution.csv', sep=';', index=None)

	PATIENT_ID_COUNT
count	7644.000000
mean	1692.536630
std	17989.725347
min	1.000000
25%	6.000000
50%	40.000000
75%	284.000000
max	919789.000000

	PATIENT_ID_COUNT
count	7644.000000
mean	1692.536630
std	17989.725347
min	1.000000
25%	6.000000
50%	40.000000
75%	284.000000
max	919789.000000

	PATIENT_ID_COUNT
count	51.000000
mean	253681.372549
std	51270.812564
min	105810.000000
25%	234124.000000
50%	258120.000000
75%	281405.000000
max	347865.000000

	PATIENT_ID_COUNT
count	51.000000
mean	253681.372549
std	51270.812564
min	105810.000000
25%	234124.000000
50%	258120.000000
75%	281405.000000
max	347865.000000

	PATIENT_SEX	MKB_CODE	ADRES	VISIT_MONTH_YEAR	AGE_CATEGORY	PATIENT_ID_COUNT	month_year
0	0	A00.0	Гурьевск	08.21	young	1	2021-08-01
1	0	A00.0	Калининград	03.20	children	1	2020-03-01
2	0	A00	Гусев	03.19	children	1	2019-03-01
3	0	A00	Калининград	01.22	children	1	2022-01-01
4	0	A00	Калининград	02.18	children	1	2018-02-01
...	...	...	...	...	...	...	...
2212388	1	Z99.1	Гурьевск	12.21	children	1	2021-12-01
2212389	1	Z99.8	Калининград	10.21	young	1	2021-10-01
2212390	1	Z99.9	Калининград	04.19	children	2	2019-04-01
2212391	1	Z99.9	Калининград	08.19	children	1	2019-08-01
2212392	1	Z99.9	Калининград	11.19	children	1	2019-11-01

	PATIENT_ID_COUNT
AGE_CATEGORY
children	4187970
young	3100989
elderly	2670328
middleage	1942558
old	888505
centenarians	147400

	PATIENT_ID_COUNT
ADRES
Калининград	8615298
Гурьевск	559127
Пионерский	306848
Советск	291640
Черняховск	263656
...	...
Ясная Поляна	3432
Ново-Московское	3409
Рыбачий	3405
Гурьевский	3337
СТ Железнодорожник	3176

	PATIENT_ID_COUNT
VISIT_MONTH_YEAR
10.21	347865
12.21	345437
11.21	328531
09.21	328436
07.21	316501
08.21	313895
04.21	313055
06.21	311045
10.19	304796
05.21	294018
04.19	290365
10.18	287364
12.19	282251
09.19	280559
02.19	279467
03.21	275785
03.19	275218
11.18	272947
11.19	269928
10.20	263960
03.18	263285
07.19	262111
03.22	261884
09.20	261412
12.18	259683
08.19	258120
04.18	258038
02.18	255036
09.18	253018
05.19	243530
08.18	242724
02.20	241591
06.19	240687
01.19	240650
07.18	239620
06.18	238109
05.18	237447
12.20	234672
03.20	233576
08.20	225479
11.20	224981
02.21	223190
01.18	221854
07.20	217798
02.22	196348
01.22	190701
01.20	189966
06.20	173373
01.21	150000
04.20	111634
05.20	105810

	MKB_CODE	PATIENT_ID_COUNT
2847	J06.9	919789
7268	Z25.8	663351
7123	Z00.0	611981
2506	I11.9	350152
3066	K02.1	316946

	PATIENT_SEX	MKB_CODE	ADRES	VISIT_MONTH_YEAR	AGE_CATEGORY	PATIENT_ID_COUNT	month_year
509571	0	J06.9	Калининград	01.18	centenarians	12	2018-01-01
509572	0	J06.9	Калининград	01.18	children	3889	2018-01-01
509573	0	J06.9	Калининград	01.18	elderly	507	2018-01-01
509574	0	J06.9	Калининград	01.18	middleage	575	2018-01-01
509575	0	J06.9	Калининград	01.18	old	153	2018-01-01
...	...	...	...	...	...	...	...
1671175	1	J06.9	Калининград	12.21	children	9169	2021-12-01
1671176	1	J06.9	Калининград	12.21	elderly	531	2021-12-01
1671177	1	J06.9	Калининград	12.21	middleage	795	2021-12-01
1671178	1	J06.9	Калининград	12.21	old	78	2021-12-01
1671179	1	J06.9	Калининград	12.21	young	2638	2021-12-01

	PATIENT_SEX	MKB_CODE	ADRES	VISIT_MONTH_YEAR	AGE_CATEGORY	PATIENT_ID_COUNT	month_year
1670874	1	J06.9	Калининград	01.18	centenarians	10	2018-01-01
1670875	1	J06.9	Калининград	01.18	children	4129	2018-01-01
1670876	1	J06.9	Калининград	01.18	elderly	198	2018-01-01
1670877	1	J06.9	Калининград	01.18	middleage	331	2018-01-01
1670878	1	J06.9	Калининград	01.18	old	39	2018-01-01
...	...	...	...	...	...	...	...
1671175	1	J06.9	Калининград	12.21	children	9169	2021-12-01
1671176	1	J06.9	Калининград	12.21	elderly	531	2021-12-01
1671177	1	J06.9	Калининград	12.21	middleage	795	2021-12-01
1671178	1	J06.9	Калининград	12.21	old	78	2021-12-01
1671179	1	J06.9	Калининград	12.21	young	2638	2021-12-01

	PATIENT_ID_COUNT
month_year
2018-01-01	12805.462069
2018-02-01	12691.392118
2018-03-01	12577.322167
2018-04-01	12463.252217
2018-05-01	12349.182266
2018-06-01	12235.112315
2018-07-01	12121.042365
2018-08-01	12006.972414
2018-09-01	11892.902463
2018-10-01	11778.832512
2018-11-01	11664.762562
2018-12-01	11550.692611
2019-01-01	11436.622660
2019-02-01	11322.552709
2019-03-01	11208.482759
2019-04-01	11094.412808
2019-05-01	10980.342857
2019-06-01	10866.272906
2019-07-01	10752.202956
2019-08-01	10638.133005
2019-09-01	10524.063054
2019-10-01	10409.993103
2019-11-01	10295.923153
2019-12-01	10181.853202
2020-01-01	10067.783251
2020-02-01	9953.713300
2020-03-01	9839.643350
2020-04-01	9725.573399
2020-05-01	9611.503448
2020-05-01	7204.478261
2020-06-01	7745.209486
2020-07-01	8285.940711
2020-08-01	8826.671937
2020-09-01	9367.403162
2020-10-01	9908.134387
2020-11-01	10448.865613
2020-12-01	10989.596838
2021-01-01	11530.328063
2021-02-01	12071.059289
2021-03-01	12611.790514
2021-04-01	13152.521739
2021-05-01	13693.252964
2021-06-01	14233.984190
2021-07-01	14774.715415
2021-08-01	15315.446640
2021-09-01	15856.177866
2021-10-01	16396.909091
2021-11-01	16937.640316
2021-12-01	17478.371542
2022-01-01	18019.102767
2022-02-01	18559.833992
2022-03-01	19100.565217

	PATIENT_ID_COUNT
month_year
2018-01-01	-720.462069
2018-02-01	3153.607882
2018-03-01	6125.677833
2018-04-01	3073.747783
2018-05-01	-4203.182266
2018-06-01	-6312.112315
2018-07-01	-6469.042365
2018-08-01	-4774.972414
2018-09-01	-18.902463
2018-10-01	2163.167488
2018-11-01	532.237438
2018-12-01	1143.307389
2019-01-01	1991.377340
2019-02-01	11303.447291
2019-03-01	2983.517241
2019-04-01	-96.412808
2019-05-01	-4283.342857
2019-06-01	-5350.272906
2019-07-01	-4842.202956
2019-08-01	-4408.133005
2019-09-01	2118.936946
2019-10-01	2912.006897
2019-11-01	-495.923153
2019-12-01	1406.146798
2020-01-01	352.216749
2020-02-01	7325.286700
2020-03-01	6853.356650
2020-04-01	-4239.573399
2020-05-01	-7223.503448
2020-05-01	-4816.478261
2020-06-01	-5271.209486
2020-07-01	-2594.940711
2020-08-01	-3375.671937
2020-09-01	1934.596838
2020-10-01	5997.865613
2020-11-01	11603.134387
2020-12-01	11916.403162
2021-01-01	-1784.328063
2021-02-01	-970.059289
2021-03-01	-1721.790514
2021-04-01	-2534.521739
2021-05-01	-5117.252964
2021-06-01	-5499.984190
2021-07-01	-4734.715415
2021-08-01	-4236.446640
2021-09-01	5188.822134
2021-10-01	6913.090909
2021-11-01	-3496.640316
2021-12-01	10336.628458
2022-01-01	352.897233
2022-02-01	2422.166008
2022-03-01	-10511.565217

	y_lag_1
month_year
2018-01	0.0
2018-02	12085.0
2018-03	15845.0
2018-04	18703.0
2018-05	15537.0
2018-06	8146.0
2018-07	5923.0
2018-08	5652.0
2018-09	7232.0
2018-10	11874.0
2018-11	13942.0
2018-12	12197.0
2019-01	12694.0
2019-02	13428.0
2019-03	22626.0
2019-04	14192.0
2019-05	10998.0
2019-06	6697.0
2019-07	5516.0
2019-08	5910.0
2019-09	6230.0
2019-10	12643.0
2019-11	13322.0
2019-12	9800.0
2020-01	11588.0
2020-02	10420.0
2020-03	17279.0
2020-04	16693.0
2020-05	5486.0
2020-06	2388.0
2020-07	2474.0
2020-08	5691.0
2020-09	5451.0
2020-10	11302.0
2020-11	15906.0
2020-12	22052.0
2021-01	22906.0
2021-02	9746.0
2021-03	11101.0
2021-04	10890.0
2021-05	10618.0
2021-06	8576.0
2021-07	8734.0
2021-08	10040.0
2021-09	11079.0
2021-10	21045.0
2021-11	23310.0
2021-12	13441.0
2022-01	27815.0
2022-02	18372.0
2022-03	20982.0

	PATIENT_ID_COUNT
PATIENT_SEX
0	7988811
1	4948939

	PATIENT_ID_COUNT	day	week	month	year
month_year
2018-01-01	12085	0	1	1	2018
2018-02-01	15845	3	5	2	2018
2018-03-01	18703	3	9	3	2018
2018-04-01	15537	6	13	4	2018
2018-05-01	8146	1	18	5	2018

	const	trend	s(2,12)	s(3,12)	s(4,12)	s(5,12)	s(6,12)	s(7,12)	s(8,12)	s(9,12)	...	s(11,12)	s(12,12)	sin(1,freq=A-DEC)	cos(1,freq=A-DEC)	sin(2,freq=A-DEC)	cos(2,freq=A-DEC)	sin(3,freq=A-DEC)	cos(3,freq=A-DEC)	sin(4,freq=A-DEC)	cos(4,freq=A-DEC)
month_year
2018-01-01	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.000000	1.000000	0.000000	1.000000	0.000000e+00	1.000000	0.000000	1.000000
2018-02-01	1.0	2.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.508671	0.860961	0.875892	0.482508	9.995463e-01	-0.030120	0.845249	-0.534373
2018-03-01	1.0	3.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.849817	0.527078	0.895839	-0.444378	9.453675e-02	-0.995521	-0.796183	-0.605056
2018-04-01	1.0	4.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.999769	0.021516	0.043022	-0.999074	-9.979172e-01	-0.064508	-0.085965	0.996298
2018-05-01	1.0	5.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.880012	-0.474951	-0.835925	-0.548843	-8.596480e-02	0.996298	0.917584	-0.397543
2018-06-01	1.0	6.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.516062	-0.856551	-0.884068	0.467359	9.984354e-01	0.055917	-0.826354	-0.563151
2018-07-01	1.0	7.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.025818	-0.999667	-0.051620	0.998667	7.738648e-02	-0.997001	-0.103102	0.994671
2018-08-01	1.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	-0.486273	-0.873807	0.849817	0.527078	-9.988797e-01	-0.047321	0.895839	-0.444378
2018-09-01	1.0	9.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	0.0	0.0	-0.863142	-0.504961	0.871706	-0.490029	-1.721336e-02	0.999852	-0.854322	-0.519744
2018-10-01	1.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	-0.999917	-0.012910	0.025818	-0.999667	9.992500e-01	0.038722	-0.051620	0.998667
2018-11-01	1.0	11.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	-0.867456	0.497513	-0.863142	-0.504961	8.606997e-03	-0.999963	0.871706	-0.490029
2018-12-01	1.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	-0.508671	0.860961	-0.875892	0.482508	-9.995463e-01	-0.030120	-0.845249	-0.534373
2019-01-01	1.0	13.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.000000	1.000000	0.000000	1.000000	0.000000e+00	1.000000	0.000000	1.000000
2019-02-01	1.0	14.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.508671	0.860961	0.875892	0.482508	9.995463e-01	-0.030120	0.845249	-0.534373
2019-03-01	1.0	15.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.849817	0.527078	0.895839	-0.444378	9.453675e-02	-0.995521	-0.796183	-0.605056
2019-04-01	1.0	16.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.999769	0.021516	0.043022	-0.999074	-9.979172e-01	-0.064508	-0.085965	0.996298
2019-05-01	1.0	17.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.880012	-0.474951	-0.835925	-0.548843	-8.596480e-02	0.996298	0.917584	-0.397543
2019-06-01	1.0	18.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.516062	-0.856551	-0.884068	0.467359	9.984354e-01	0.055917	-0.826354	-0.563151
2019-07-01	1.0	19.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.025818	-0.999667	-0.051620	0.998667	7.738648e-02	-0.997001	-0.103102	0.994671
2019-08-01	1.0	20.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	-0.486273	-0.873807	0.849817	0.527078	-9.988797e-01	-0.047321	0.895839	-0.444378
2019-09-01	1.0	21.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	0.0	0.0	-0.863142	-0.504961	0.871706	-0.490029	-1.721336e-02	0.999852	-0.854322	-0.519744
2019-10-01	1.0	22.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	-0.999917	-0.012910	0.025818	-0.999667	9.992500e-01	0.038722	-0.051620	0.998667
2019-11-01	1.0	23.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	-0.867456	0.497513	-0.863142	-0.504961	8.606997e-03	-0.999963	0.871706	-0.490029
2019-12-01	1.0	24.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	-0.508671	0.860961	-0.875892	0.482508	-9.995463e-01	-0.030120	-0.845249	-0.534373
2020-01-01	1.0	25.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.000000	1.000000	0.000000	1.000000	0.000000e+00	1.000000	0.000000	1.000000
2020-02-01	1.0	26.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.507415	0.861702	0.874481	0.485060	9.996685e-01	-0.025748	0.848351	-0.529434
2020-03-01	1.0	27.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.857315	0.514793	0.882679	-0.469977	5.147875e-02	-0.998674	-0.829677	-0.558244
2020-04-01	1.0	28.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.999963	0.008583	0.017166	-0.999853	-9.996685e-01	-0.025748	-0.034328	0.999411
2020-05-01	1.0	29.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.874481	-0.485060	-0.848351	-0.529434	-5.147875e-02	0.998674	0.898292	-0.439400
2020-06-01	1.0	30.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.507415	-0.861702	-0.874481	0.485060	9.996685e-01	0.025748	-0.848351	-0.529434
2020-07-01	1.0	31.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.017166	-0.999853	-0.034328	0.999411	5.147875e-02	-0.998674	-0.068615	0.997643
2020-08-01	1.0	32.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	-0.492548	-0.870285	0.857315	0.514793	-9.996685e-01	-0.025748	0.882679	-0.469977
2020-09-01	1.0	33.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	0.0	0.0	-0.866025	-0.500000	0.866025	-0.500000	-4.898587e-16	1.000000	-0.866025	-0.500000
2020-10-01	1.0	34.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	-0.999963	-0.008583	0.017166	-0.999853	9.996685e-01	0.025748	-0.034328	0.999411
2020-11-01	1.0	35.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	-0.866025	0.500000	-0.866025	-0.500000	6.123234e-16	-1.000000	0.866025	-0.500000
2020-12-01	1.0	36.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	-0.507415	0.861702	-0.874481	0.485060	-9.996685e-01	-0.025748	-0.848351	-0.529434
2021-01-01	1.0	37.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.000000	1.000000	0.000000	1.000000	0.000000e+00	1.000000	0.000000	1.000000
2021-02-01	1.0	38.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.508671	0.860961	0.875892	0.482508	9.995463e-01	-0.030120	0.845249	-0.534373
2021-03-01	1.0	39.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.849817	0.527078	0.895839	-0.444378	9.453675e-02	-0.995521	-0.796183	-0.605056
2021-04-01	1.0	40.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.999769	0.021516	0.043022	-0.999074	-9.979172e-01	-0.064508	-0.085965	0.996298
2021-05-01	1.0	41.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.880012	-0.474951	-0.835925	-0.548843	-8.596480e-02	0.996298	0.917584	-0.397543
2021-06-01	1.0	42.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.516062	-0.856551	-0.884068	0.467359	9.984354e-01	0.055917	-0.826354	-0.563151
2021-07-01	1.0	43.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.025818	-0.999667	-0.051620	0.998667	7.738648e-02	-0.997001	-0.103102	0.994671
2021-08-01	1.0	44.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	-0.486273	-0.873807	0.849817	0.527078	-9.988797e-01	-0.047321	0.895839	-0.444378
2021-09-01	1.0	45.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	...	0.0	0.0	-0.863142	-0.504961	0.871706	-0.490029	-1.721336e-02	0.999852	-0.854322	-0.519744
2021-10-01	1.0	46.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	-0.999917	-0.012910	0.025818	-0.999667	9.992500e-01	0.038722	-0.051620	0.998667
2021-11-01	1.0	47.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	-0.867456	0.497513	-0.863142	-0.504961	8.606997e-03	-0.999963	0.871706	-0.490029
2021-12-01	1.0	48.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	-0.508671	0.860961	-0.875892	0.482508	-9.995463e-01	-0.030120	-0.845249	-0.534373
2022-01-01	1.0	49.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.000000	1.000000	0.000000	1.000000	0.000000e+00	1.000000	0.000000	1.000000
2022-02-01	1.0	50.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.508671	0.860961	0.875892	0.482508	9.995463e-01	-0.030120	0.845249	-0.534373
2022-03-01	1.0	51.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.849817	0.527078	0.895839	-0.444378	9.453675e-02	-0.995521	-0.796183	-0.605056

	tavg	tmin	tmax	prcp	wspd	pres	tsun
time
2018-01-01	0.1	-2.1	2.2	79.4	13.0	NaN	NaN
2018-02-01	-4.3	-6.9	-0.7	22.1	NaN	NaN	NaN
2018-03-01	-0.8	-3.5	3.0	20.6	NaN	NaN	NaN
2018-04-01	10.3	5.4	17.2	43.7	NaN	NaN	NaN
2018-05-01	15.9	10.2	22.9	35.5	10.5	NaN	NaN
2018-06-01	17.2	12.2	23.2	34.4	NaN	NaN	NaN
2018-07-01	20.3	16.5	25.5	94.2	NaN	1012.7	NaN
2018-08-01	19.6	14.7	25.4	65.9	10.0	1015.7	NaN
2018-09-01	15.3	10.4	21.3	40.0	10.0	1017.8	NaN
2018-10-01	10.3	5.1	15.4	61.6	12.9	1015.8	NaN
2018-11-01	4.5	2.1	6.9	29.5	11.1	1024.6	NaN
2018-12-01	1.9	-0.2	3.3	86.5	12.0	1014.7	NaN
2019-01-01	-1.4	-4.4	0.9	87.4	14.6	1007.5	NaN
2019-02-01	3.0	0.6	5.6	37.2	15.3	1017.8	NaN
2019-03-01	4.7	1.3	8.9	56.5	16.6	1009.8	NaN
2019-04-01	8.4	3.9	15.4	3.5	13.2	1020.8	NaN
2019-05-01	11.9	7.9	17.3	56.9	13.0	1012.5	NaN
2019-06-01	20.1	14.3	27.2	65.7	11.1	1017.5	NaN
2019-07-01	17.6	13.3	22.3	79.0	13.2	1011.6	NaN
2019-08-01	18.6	13.1	24.8	NaN	9.2	1016.4	NaN
2019-09-01	14.1	9.5	18.7	NaN	12.8	1014.5	NaN
2019-10-01	10.7	6.9	14.2	NaN	11.5	1012.6	NaN
2019-11-01	6.0	3.0	8.1	NaN	12.9	1009.5	NaN
2019-12-01	4.1	1.5	5.9	55.2	14.4	1009.5	NaN
2020-01-01	4.5	1.8	6.0	NaN	16.4	1016.5	NaN
2020-02-01	4.2	1.1	7.3	79.5	17.4	1005.7	NaN
2020-03-01	4.6	0.6	9.0	47.5	13.9	1016.9	NaN
2020-04-01	7.5	1.7	13.8	8.7	15.3	1016.4	NaN
2020-05-01	10.4	5.6	15.9	86.7	14.6	1016.2	NaN
2020-06-01	17.9	13.0	23.6	93.2	10.7	1012.5	NaN
2020-07-01	17.4	12.0	22.9	NaN	13.4	1013.5	NaN
2020-08-01	19.0	13.5	23.8	NaN	10.8	1013.8	NaN
2020-09-01	15.7	11.3	20.3	NaN	14.6	1015.3	NaN
2020-10-01	11.1	7.9	14.0	NaN	14.3	1011.5	NaN
2020-11-01	7.2	4.8	9.2	NaN	16.5	1020.6	NaN
2020-12-01	2.2	0.3	3.8	NaN	17.3	1013.2	NaN
2021-01-01	-1.3	-3.5	0.7	NaN	14.0	1008.1	NaN
2021-02-01	-3.0	-7.0	0.7	NaN	13.9	1020.2	NaN
2021-03-01	3.3	-0.2	6.5	NaN	17.8	1016.2	NaN
2021-04-01	6.0	1.2	10.2	NaN	17.5	1014.0	NaN
2021-05-01	11.6	6.6	15.9	NaN	15.7	1010.0	NaN
2021-06-01	18.7	12.6	23.3	NaN	12.0	1016.9	NaN
2021-07-01	21.7	16.5	26.2	NaN	11.8	1013.0	NaN
2021-08-01	17.0	13.2	20.8	NaN	13.8	1011.9	NaN
2021-09-01	13.5	9.6	17.3	NaN	14.5	1017.5	NaN
2021-10-01	9.5	5.4	13.7	NaN	15.6	1017.2	NaN
2021-11-01	5.8	3.5	7.8	NaN	17.7	1011.3	NaN
2021-12-01	-0.8	-3.1	1.3	NaN	17.4	1011.9	NaN
2022-01-01	2.2	-0.1	4.1	NaN	23.8	1011.7	NaN
2022-02-01	3.3	0.8	5.5	NaN	21.7	1007.4	NaN
2022-03-01	2.3	-2.5	7.5	NaN	13.4	1027.5	NaN
2022-04-01	6.6	2.4	10.5	NaN	18.1	1012.3	NaN

	PATIENT_ID_COUNT
month
2018-01-01	221854
2018-02-01	255036
2018-03-01	263285
2018-04-01	258038
2018-05-01	237447
2018-06-01	238109
2018-07-01	239620
2018-08-01	242724
2018-09-01	253018
2018-10-01	287364
2018-11-01	272947
2018-12-01	259683
2019-01-01	240650
2019-02-01	279467
2019-03-01	275218
2019-04-01	290365
2019-05-01	243530
2019-06-01	240687
2019-07-01	262111
2019-08-01	258120
2019-09-01	280559
2019-10-01	304796
2019-11-01	269928
2019-12-01	282251
2020-01-01	189966
2020-02-01	241591
2020-03-01	233576
2020-04-01	111634
2020-05-01	105810
2020-06-01	173373
2020-07-01	217798
2020-08-01	225479
2020-09-01	261412
2020-10-01	263960
2020-11-01	224981
2020-12-01	234672
2021-01-01	150000
2021-02-01	223190
2021-03-01	275785
2021-04-01	313055
2021-05-01	294018
2021-06-01	311045
2021-07-01	316501
2021-08-01	313895
2021-09-01	328436
2021-10-01	347865
2021-11-01	328531
2021-12-01	345437
2022-01-01	190701
2022-02-01	196348
2022-03-01	261884